Postdoctoral Scholar at the University of California Santa Barbara
The Yu Emotion Science, or YES Lab (https://yeslab.psych.ucsb.edu/), within the Department of Psychological and Brain Sciences at the University of California, Santa Barbara (https://psych.ucsb.edu/), is seeking applications for an open, full-time Postdoctoral Scholar position with an anticipated start date of July 1, 2022, although the start date is flexible.
The Postdoctoral Scholar will work closely with the Principal Investigator, Dr. Hongbo Yu, to establish the lab in its new home at UCSB. The duties of the Postdoctoral Scholar will include study design and execution (e.g., programming online and laboratory tasks, collecting and analyzing behavioral and neuroimaging data), writing up research papers and grant applications, presenting research projects within the department and on conferences, and supervising undergraduate and graduate students.
The YES Lab seeks to understand the relation between emotion and morality, and how they are implemented in the brain. To answer these questions, we adopt a wide range of methods, including behavioral experiments, neuroimaging, computational modeling, psychophysiological recording (e.g., eye-tracking, skin conductance, etc.), and natural language processing. We also emphasize an interdisciplinary approach. Our research is inspired by and has implications for philosophy, behavioral economics, psychiatry, history, and anthropology.
The Postdoctoral Scholar will have the opportunity to build connections with stellar scientists within the broader area of brain/mind research by participating in activities at the SAGE Center for the Study of the Mind (https://www.sagecenter.ucsb.edu). Successful applicants will also have the opportunities to engage further with the SAGE Center through the SAGE Junior Fellows Program (https://www.sagecenter.ucsb.edu/fellowships).
The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through research, teaching, and service as appropriate to the position
Apply link: https://recruit.ap.ucsb.edu/JPF02144
Help contact: [email protected]
QUALIFICATIONS
Basic qualifications (required at time of application)
Applicants must have completed all requirements for a PhD (or equivalent) except the dissertation at the time of application in psychology, cognitive neuroscience, computer science, or a related field.
Additional qualifications (required at time of start)
Five years minimum research experience (including the years towards doctoral degree) in psychology, cognitive neuroscience, computer science, or a related field, supported by a strong publication record, is required.
PhD must be conferred by the start of appointment.
Preferred qualifications
Applicants with training and experience in a subset of these areas will be given priority: advanced functional MRI analysis (e.g., pattern-based classification, representational similarity analysis, functional connectivity) and data sharing (e.g., BIDS and OpenNeuro), computational modeling of behavioral and neural data, natural language processing, strong theoretical training in social psychology, moral cognition and moral philosophy. The qualified applicant should be proficient in at least one, preferably multiple, programming language (e.g., MATLAB, Python, R) and be willing to learn others when needed.
APPLICATION REQUIREMENTS
Document requirements
Curriculum Vitae - Your most recently updated C.V.
Cover Letter - Describing your qualifications, research interests and career goals
Representative Publication #1
Representative Publication #2
Reference requirements
2 required (contact information only)
Applicants that are strongly considered will have their references contacted prior to selection for interview.
Apply link: https://recruit.ap.ucsb.edu/JPF02144
Help contact: [email protected]
CAMPUS INFORMATION
The Yu Emotion Science, or YES Lab (https://yeslab.psych.ucsb.edu/), within the Department of Psychological and Brain Sciences at the University of California, Santa Barbara (https://psych.ucsb.edu/), is seeking applications for an open, full-time Postdoctoral Scholar position with an anticipated start date of July 1, 2022, although the start date is flexible.
The Postdoctoral Scholar will work closely with the Principal Investigator, Dr. Hongbo Yu, to establish the lab in its new home at UCSB. The duties of the Postdoctoral Scholar will include study design and execution (e.g., programming online and laboratory tasks, collecting and analyzing behavioral and neuroimaging data), writing up research papers and grant applications, presenting research projects within the department and on conferences, and supervising undergraduate and graduate students.
The YES Lab seeks to understand the relation between emotion and morality, and how they are implemented in the brain. To answer these questions, we adopt a wide range of methods, including behavioral experiments, neuroimaging, computational modeling, psychophysiological recording (e.g., eye-tracking, skin conductance, etc.), and natural language processing. We also emphasize an interdisciplinary approach. Our research is inspired by and has implications for philosophy, behavioral economics, psychiatry, history, and anthropology.
The Postdoctoral Scholar will have the opportunity to build connections with stellar scientists within the broader area of brain/mind research by participating in activities at the SAGE Center for the Study of the Mind (https://www.sagecenter.ucsb.edu). Successful applicants will also have the opportunities to engage further with the SAGE Center through the SAGE Junior Fellows Program (https://www.sagecenter.ucsb.edu/fellowships).
The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through research, teaching, and service as appropriate to the position
Apply link: https://recruit.ap.ucsb.edu/JPF02144
Help contact: [email protected]
QUALIFICATIONS
Basic qualifications (required at time of application)
Applicants must have completed all requirements for a PhD (or equivalent) except the dissertation at the time of application in psychology, cognitive neuroscience, computer science, or a related field.
Additional qualifications (required at time of start)
Five years minimum research experience (including the years towards doctoral degree) in psychology, cognitive neuroscience, computer science, or a related field, supported by a strong publication record, is required.
PhD must be conferred by the start of appointment.
Preferred qualifications
Applicants with training and experience in a subset of these areas will be given priority: advanced functional MRI analysis (e.g., pattern-based classification, representational similarity analysis, functional connectivity) and data sharing (e.g., BIDS and OpenNeuro), computational modeling of behavioral and neural data, natural language processing, strong theoretical training in social psychology, moral cognition and moral philosophy. The qualified applicant should be proficient in at least one, preferably multiple, programming language (e.g., MATLAB, Python, R) and be willing to learn others when needed.
APPLICATION REQUIREMENTS
Document requirements
Curriculum Vitae - Your most recently updated C.V.
Cover Letter - Describing your qualifications, research interests and career goals
Representative Publication #1
Representative Publication #2
Reference requirements
2 required (contact information only)
Applicants that are strongly considered will have their references contacted prior to selection for interview.
Apply link: https://recruit.ap.ucsb.edu/JPF02144
Help contact: [email protected]
CAMPUS INFORMATION
yeslab.psych.ucsb.edu
Yu Emotion Science | Psychological & Brain Sciences | UC Santa Barbara
We chart the moral landscape of individuals and societies to understand how emotions emerge from “upheavals of thought”.
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
As a condition of employment, you will be required to comply with the University of California SARS-CoV-2 (COVID-19) Vaccination Program Policy https://policy.ucop.edu/doc/5000695/SARS-CoV-2_Covid-19. All Covered Individuals under the policy must provide proof of Full Vaccination or, if applicable, submit a request for Exception (based on Medical Exemption, Disability, and/or Religious Objection) or Deferral (based on pregnancy) no later than the applicable deadline. New University of California employees must (a) provide proof of receiving at least one dose of a COVID-19 Vaccine no later than 14 calendar days after their first date of employment and provide proof of Full Vaccination no later than eight weeks after their first date of employment; or (b) if applicable, submit a request for Exception or Deferral no later than 14 calendar days after their first date of employment. (Capitalized terms in this paragraph are defined in the policy.) Federal, state, or local public health directives may impose additional requirements.
JOB LOCATION
Santa Barbara, CA
As a condition of employment, you will be required to comply with the University of California SARS-CoV-2 (COVID-19) Vaccination Program Policy https://policy.ucop.edu/doc/5000695/SARS-CoV-2_Covid-19. All Covered Individuals under the policy must provide proof of Full Vaccination or, if applicable, submit a request for Exception (based on Medical Exemption, Disability, and/or Religious Objection) or Deferral (based on pregnancy) no later than the applicable deadline. New University of California employees must (a) provide proof of receiving at least one dose of a COVID-19 Vaccine no later than 14 calendar days after their first date of employment and provide proof of Full Vaccination no later than eight weeks after their first date of employment; or (b) if applicable, submit a request for Exception or Deferral no later than 14 calendar days after their first date of employment. (Capitalized terms in this paragraph are defined in the policy.) Federal, state, or local public health directives may impose additional requirements.
JOB LOCATION
Santa Barbara, CA
Fully-funded Ph.D. student position in Interactive Music Systems using Machine Learning and AI at Chalmers in Sweden
Dear all,
We would like to remind that the deadline for this Ph.D. position is soon: 18 December, 2021.
We are excited to share our new Ph.D. student position in Interactive Music Systems using Machine Learning and AI at Chalmers University of Technology in Gothenburg, Sweden. The research focus is understanding interactivity in interdisciplinary musical practices. The selected candidate will take part in the development of novel interactive systems of machine learning and artificial intelligence for musical applications such as live performances, artwork installations and musical production. This position is fully-funded for five years, which is the full duration of the studies, covering 80% research and 20% teaching duties.
The position is hosted at the Interaction Design Unit (IxD) Computer Science and Engineering (CSE) Department The CSE department is now expanding the IxD division with a new research group in Interactive AI in Music. The current members of this new research group are Assistant Professor Kıvanç Tatar and Professor Palle Dahlstedt. The research perspective centers on aesthetics, ethics, and societal aspects of Artificial Intelligence. The creation of this new research group, as well as this Ph.D. position, is funded by the The Wallenberg AI, Autonomous Systems and Software Program – Humanities and Society (WASP-HS).
Please feel free to contact me ([email protected]) or Palle Dahlstedt ([email protected]) if you have questions, and please do share the call to people who might be interested.
We are looking forward to receiving your applications. The full details of this position and the link to the application portal can be found at the official call page:
https://www.chalmers.se/en/about-chalmers/Working-at-Chalmers/Vacancies/Pages/default.aspx?rmpage=job&rmjob=9976&rmlang=UK
Thank you,
PS: Apologies for cross-posting.
--
Kıvanç Tatar, Ph.D., M.Mus., B.Sc.
Musician|Artist|Researcher in Artificial Intelligence for Music and Interactive Arts
+
Assistant Professor in Interactive AI
Department of Computer Science and Engineering | Interaction Design Unit
Chalmers University of Technology
Gothenburg, Sweden
+
WASP-HS Fellow
The Wallenberg AI, Autonomous Systems and Software Program - Humanities and Society
https://kivanctatar.com/
Dear all,
We would like to remind that the deadline for this Ph.D. position is soon: 18 December, 2021.
We are excited to share our new Ph.D. student position in Interactive Music Systems using Machine Learning and AI at Chalmers University of Technology in Gothenburg, Sweden. The research focus is understanding interactivity in interdisciplinary musical practices. The selected candidate will take part in the development of novel interactive systems of machine learning and artificial intelligence for musical applications such as live performances, artwork installations and musical production. This position is fully-funded for five years, which is the full duration of the studies, covering 80% research and 20% teaching duties.
The position is hosted at the Interaction Design Unit (IxD) Computer Science and Engineering (CSE) Department The CSE department is now expanding the IxD division with a new research group in Interactive AI in Music. The current members of this new research group are Assistant Professor Kıvanç Tatar and Professor Palle Dahlstedt. The research perspective centers on aesthetics, ethics, and societal aspects of Artificial Intelligence. The creation of this new research group, as well as this Ph.D. position, is funded by the The Wallenberg AI, Autonomous Systems and Software Program – Humanities and Society (WASP-HS).
Please feel free to contact me ([email protected]) or Palle Dahlstedt ([email protected]) if you have questions, and please do share the call to people who might be interested.
We are looking forward to receiving your applications. The full details of this position and the link to the application portal can be found at the official call page:
https://www.chalmers.se/en/about-chalmers/Working-at-Chalmers/Vacancies/Pages/default.aspx?rmpage=job&rmjob=9976&rmlang=UK
Thank you,
PS: Apologies for cross-posting.
--
Kıvanç Tatar, Ph.D., M.Mus., B.Sc.
Musician|Artist|Researcher in Artificial Intelligence for Music and Interactive Arts
+
Assistant Professor in Interactive AI
Department of Computer Science and Engineering | Interaction Design Unit
Chalmers University of Technology
Gothenburg, Sweden
+
WASP-HS Fellow
The Wallenberg AI, Autonomous Systems and Software Program - Humanities and Society
https://kivanctatar.com/
Kivanctatar
Kıvanç Tatar
Kıvanç Tatar is a musician, new media artist, and researcher focusing on Artificial Intelligence for Music and Interactive Media.
PhD Programme in Machine Learning at IIST
Dear all,
The Indian Institute of Space Science and Technology (IIST) Thiruvananthapuram, invites applications for PhD programme in the field of Machine Learning. For more details please visit www.iist.ac.in
Regards,
Dr. S. Sumitra
Associate Professor
Department of Mathematics
IIST
Dear all,
The Indian Institute of Space Science and Technology (IIST) Thiruvananthapuram, invites applications for PhD programme in the field of Machine Learning. For more details please visit www.iist.ac.in
Regards,
Dr. S. Sumitra
Associate Professor
Department of Mathematics
IIST
PhD Studentships (Graduate Teaching
Assistants)
****** Apologies for cross-posting. Please feel free to redistribute. ******
PhD studentships (Graduate Teaching Assistant) are available at Edge
Hill University, UK. The deadline for the application is on Monday 17
January 2022. For more information, including eligibility, entry
requirements and how to apply, navigate to
https://urldefense.com/v3/__https://jobs.edgehill.ac.uk/vacancy.aspx?ref=EHGT253-1121-N__;!!LIr3w8kk_Xxm!6Kohm1T05W7K9B3oR59DJi6vMwr26X-RPplOIa-Kpwq1sQfGQAA0lu86Wkl1QGXdVgyO-T7e$
You are encouraged to discuss your choice of research topics with me
([email protected]) before applying. Your PhD topics can be in
the areas of #deeplearning #comutervision #ai #robotics
#videoanalytics #machinelearning
Selected recent publications are:
1. An attention-driven hierarchical multi-scale representation for
visual recognition, BMVC 2021.
2. Attend and Guide (AG-Net): A Keypoints-Driven Attention-Based Deep
Network for Image Recognition, IEEE Trans. on Image Processing 2021.
3. Context-aware Attentional Pooling (CAP) for Fine-grained Visual
Classification. AAAI 2021.
4. Attentional Learn-able Pooling for Human Activity Recognition, ICRA 2021.
5. Coarse Temporal Attention Network (CTA-Net) for Driver’s Activity
Recognition. WACV 2021.
6. Orderly Disorder in Point Cloud Domain. ECCV 2020
7. Unsupervised Monocular Depth Estimation for Night-time Images using
Adversarial Domain Feature Adaptation. ECCV 2020
8. Rotation Axis Focused Attention Network (RAFA-Net) for Estimating
Head Pose. ACCV 2020.
9. Regional Attention Network (RAN) for Head Pose and Fine-grained
Gesture Recognition. IEEE Trans. on Affective Computing 2020.
10. Deep CNN, Body Pose and Body-Object Interaction Features for
Drivers' Activity Monitoring. IEEE Trans. on Intelligent
Transportation Systems 2020.
Kind regards,
Ardhendu
--------------------------
Dr Ardhendu Behera
Reader (Associate Professor) in Computer Vision & AI
Department of Computer Science
Edge Hill University, Ormskirk, Lancashire, L39 4QP
Publications (Google Scholar)
https://urldefense.com/v3/__https://www.edgehill.ac.uk/computerscience/__;!!LIr3w8kk_Xxm!6Kohm1T05W7K9B3oR59DJi6vMwr26X-RPplOIa-Kpwq1sQfGQAA0lu86Wkl1QGXdVucozWU3$
https://urldefense.com/v3/__https://computing.edgehill.ac.uk/*abehera/__;fg!!LIr3w8kk_Xxm!6Kohm1T05W7K9B3oR59DJi6vMwr26X-RPplOIa-Kpwq1sQfGQAA0lu86Wkl1QGXdVjUWgB3F$
T: +44 (0) 1695 65 7270
Assistants)
****** Apologies for cross-posting. Please feel free to redistribute. ******
PhD studentships (Graduate Teaching Assistant) are available at Edge
Hill University, UK. The deadline for the application is on Monday 17
January 2022. For more information, including eligibility, entry
requirements and how to apply, navigate to
https://urldefense.com/v3/__https://jobs.edgehill.ac.uk/vacancy.aspx?ref=EHGT253-1121-N__;!!LIr3w8kk_Xxm!6Kohm1T05W7K9B3oR59DJi6vMwr26X-RPplOIa-Kpwq1sQfGQAA0lu86Wkl1QGXdVgyO-T7e$
You are encouraged to discuss your choice of research topics with me
([email protected]) before applying. Your PhD topics can be in
the areas of #deeplearning #comutervision #ai #robotics
#videoanalytics #machinelearning
Selected recent publications are:
1. An attention-driven hierarchical multi-scale representation for
visual recognition, BMVC 2021.
2. Attend and Guide (AG-Net): A Keypoints-Driven Attention-Based Deep
Network for Image Recognition, IEEE Trans. on Image Processing 2021.
3. Context-aware Attentional Pooling (CAP) for Fine-grained Visual
Classification. AAAI 2021.
4. Attentional Learn-able Pooling for Human Activity Recognition, ICRA 2021.
5. Coarse Temporal Attention Network (CTA-Net) for Driver’s Activity
Recognition. WACV 2021.
6. Orderly Disorder in Point Cloud Domain. ECCV 2020
7. Unsupervised Monocular Depth Estimation for Night-time Images using
Adversarial Domain Feature Adaptation. ECCV 2020
8. Rotation Axis Focused Attention Network (RAFA-Net) for Estimating
Head Pose. ACCV 2020.
9. Regional Attention Network (RAN) for Head Pose and Fine-grained
Gesture Recognition. IEEE Trans. on Affective Computing 2020.
10. Deep CNN, Body Pose and Body-Object Interaction Features for
Drivers' Activity Monitoring. IEEE Trans. on Intelligent
Transportation Systems 2020.
Kind regards,
Ardhendu
--------------------------
Dr Ardhendu Behera
Reader (Associate Professor) in Computer Vision & AI
Department of Computer Science
Edge Hill University, Ormskirk, Lancashire, L39 4QP
Publications (Google Scholar)
https://urldefense.com/v3/__https://www.edgehill.ac.uk/computerscience/__;!!LIr3w8kk_Xxm!6Kohm1T05W7K9B3oR59DJi6vMwr26X-RPplOIa-Kpwq1sQfGQAA0lu86Wkl1QGXdVucozWU3$
https://urldefense.com/v3/__https://computing.edgehill.ac.uk/*abehera/__;fg!!LIr3w8kk_Xxm!6Kohm1T05W7K9B3oR59DJi6vMwr26X-RPplOIa-Kpwq1sQfGQAA0lu86Wkl1QGXdVjUWgB3F$
T: +44 (0) 1695 65 7270
Jobs at Edge Hill University
Job Vacancy at Edge Hill University: Graduate Teaching Assistant (GTA) PhD Studentships - Computer Science
A fantastic opportunity to study for a fully funded doctorate whilst gaining valuable teaching experienceAbout YouYou will have a 2.1 or above undergraduate degree in a relevant subject, whilst a Masters degree would also be an advantage. You should...
Research Fellow/Senior Research Fellow in Computer Vision, Spatial Audio and Audio-Visual AI
Vision, Speech & Signal Processing
Location: Guildford
Salary: £33,309 to £50,296 per annum
Fixed Term
Post Type: Full Time
Closing Date: 23.59 hours GMT on Friday 17 December 2021
Reference: 073621
Join a new research partnership with the BBC at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey.
This is an exciting opportunity for an outstanding researcher in Computer Vision, Audio and/or Audio-Visual AI to join CVSSP as part of a major new five-year research partnership with the BBC to realise Future Personalised Media Experiences.
The goal of the research partnership is to realise future personalised content creation and delivery at scale for the public at home or on the move. CVSSP research will address the key challenges for personalised content creation and rendering by advancing computer vision, audio analysis and audio-visual AI to transform captured 2D video to object-based media. Research will advance automatic online understanding, reconstruction and neural rendering of complex dynamic real-world scenes and events. This will enable a new generation of personalised media content which adapts to user requirements and interests. The new partnership with the BBC and creative industry partners will position the UK to lead future personalised media experiences.
The Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey is ranked first in the UK for computer vision and recognised worldwide for pioneering new technologies in audio and vision. The centre leads ground-breaking research in audio-visual AI and machine perception for the benefit of people and society through technological innovations in healthcare, security, entertainment, robotics and communications. Over the past two decades, CVSSP has pioneered advances in 3D and 4D computer vision and spatial audio which have enabled award-winning technologies for content production in TV, film, games and immersive entertainment.
BBC R&D (bbc.co.uk/rd) has a worldwide reputation for developments in media technology going back over 90 years and has worked closely with CVSSP for over 20 years. It has pioneered the development of object-based media, working closely with programme-makers and technology teams across the BBC. Recent work has included object-based audio delivery across multiple synchronised devices for sports and drama, and AI for recognising wildlife for natural history.
The Research Fellow B will be an experienced researcher with an excellent track-record of publication in leading academic forums and post-doctoral research leadership. The successful candidate will take an active role in leading the research programme, contributing novel machine learning approaches to real-world dynamic scene understanding and reconstruction from video, and co-supervision of post-doctoral and PhD researchers.
The Research Fellow A will hold a PhD in computer vision, audio and/or audio-visual AI with a track-record of publication in leading academic forums. The successful candidate will contribute novel machine learning approaches advancing audio-visual AI to transform video of real-world scenes to object-based representation and neural rendering. The post-holder will collaborate with the team and project partners to realise personalised media experiences.
The post is at the core of a research team working together with the BBC, University and industry partners to realise personalised object-based media experiences at scale for offline content and live events. These posts will enable individuals to advance knowledge in computer vision, audio and machine learning and raise their own academic and research profile by joining Europe’s largest research centre in this field. All posts will initially be offered for a fixed term contract for up 3 years which is extendable for the 5-year duration of the partnership.
Vision, Speech & Signal Processing
Location: Guildford
Salary: £33,309 to £50,296 per annum
Fixed Term
Post Type: Full Time
Closing Date: 23.59 hours GMT on Friday 17 December 2021
Reference: 073621
Join a new research partnership with the BBC at the Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey.
This is an exciting opportunity for an outstanding researcher in Computer Vision, Audio and/or Audio-Visual AI to join CVSSP as part of a major new five-year research partnership with the BBC to realise Future Personalised Media Experiences.
The goal of the research partnership is to realise future personalised content creation and delivery at scale for the public at home or on the move. CVSSP research will address the key challenges for personalised content creation and rendering by advancing computer vision, audio analysis and audio-visual AI to transform captured 2D video to object-based media. Research will advance automatic online understanding, reconstruction and neural rendering of complex dynamic real-world scenes and events. This will enable a new generation of personalised media content which adapts to user requirements and interests. The new partnership with the BBC and creative industry partners will position the UK to lead future personalised media experiences.
The Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey is ranked first in the UK for computer vision and recognised worldwide for pioneering new technologies in audio and vision. The centre leads ground-breaking research in audio-visual AI and machine perception for the benefit of people and society through technological innovations in healthcare, security, entertainment, robotics and communications. Over the past two decades, CVSSP has pioneered advances in 3D and 4D computer vision and spatial audio which have enabled award-winning technologies for content production in TV, film, games and immersive entertainment.
BBC R&D (bbc.co.uk/rd) has a worldwide reputation for developments in media technology going back over 90 years and has worked closely with CVSSP for over 20 years. It has pioneered the development of object-based media, working closely with programme-makers and technology teams across the BBC. Recent work has included object-based audio delivery across multiple synchronised devices for sports and drama, and AI for recognising wildlife for natural history.
The Research Fellow B will be an experienced researcher with an excellent track-record of publication in leading academic forums and post-doctoral research leadership. The successful candidate will take an active role in leading the research programme, contributing novel machine learning approaches to real-world dynamic scene understanding and reconstruction from video, and co-supervision of post-doctoral and PhD researchers.
The Research Fellow A will hold a PhD in computer vision, audio and/or audio-visual AI with a track-record of publication in leading academic forums. The successful candidate will contribute novel machine learning approaches advancing audio-visual AI to transform video of real-world scenes to object-based representation and neural rendering. The post-holder will collaborate with the team and project partners to realise personalised media experiences.
The post is at the core of a research team working together with the BBC, University and industry partners to realise personalised object-based media experiences at scale for offline content and live events. These posts will enable individuals to advance knowledge in computer vision, audio and machine learning and raise their own academic and research profile by joining Europe’s largest research centre in this field. All posts will initially be offered for a fixed term contract for up 3 years which is extendable for the 5-year duration of the partnership.
The University of Surrey is a global university with a world-class research profile and an enterprising spirit, located in one of the safest counties in England, within 35 minutes of London by train and minutes away from the Surrey Hills, an Area of Outstanding Natural Beauty. Recent investments have seen the opening of a world-class Sports Park and important updates to central facilities.
We can offer a generous renumeration package, which includes relocation assistance where appropriate, an attractive research environment, the latest teaching facilities and access to a variety of staff development opportunities.
How to apply
Informal enquiries are welcomed by Professor Adrian Hilton by email ([email protected]) or via the University of Surrey jobs website https://jobs.surrey.ac.uk/Vacancies.aspx
Further details and the application portal can be found from below:
https://jobs.surrey.ac.uk/vacancy.aspx?ref=073621
Please feel free to share this advert to those who might be interested.
Thanks for your attentions.
Best wishes,
Wenwu
--
Professor Wenwu Wang
Centre for Vision Speech and Signal Processing
Department of Electronic Engineering
University of Surrey
Guildford GU2 7XH
United Kingdom
Phone: +44 (0) 1483 686039
Fax: +44 (0) 1483 686031
Email: [email protected]
https://personal.ee.surrey.ac.uk/Personal/W.Wang/
We can offer a generous renumeration package, which includes relocation assistance where appropriate, an attractive research environment, the latest teaching facilities and access to a variety of staff development opportunities.
How to apply
Informal enquiries are welcomed by Professor Adrian Hilton by email ([email protected]) or via the University of Surrey jobs website https://jobs.surrey.ac.uk/Vacancies.aspx
Further details and the application portal can be found from below:
https://jobs.surrey.ac.uk/vacancy.aspx?ref=073621
Please feel free to share this advert to those who might be interested.
Thanks for your attentions.
Best wishes,
Wenwu
--
Professor Wenwu Wang
Centre for Vision Speech and Signal Processing
Department of Electronic Engineering
University of Surrey
Guildford GU2 7XH
United Kingdom
Phone: +44 (0) 1483 686039
Fax: +44 (0) 1483 686031
Email: [email protected]
https://personal.ee.surrey.ac.uk/Personal/W.Wang/
jobs.surrey.ac.uk
Current Vacancies - Jobs at the University of Surrey
[No Meta Tag Description provided] Vacancies
Fully Funded PhD and Research Associate Position
Dear colleagues,
Cross-Caps Lab at IIIT Delhi, India has openings for outstanding students with an interest in speech/audio processing, machine-learning and deep-learning. To initiate discussion, please send an expression of interest email with the subject "Interest: PhD/RA with Cross-Caps" at abrol[at]iiitd.ac.in with your CV if you have relevant research experience.
Cross-Caps Lab: https://bit.ly/38UqDk1
PhD Openings:
This PhD opening is suitable for a candidate with a strong background in one of these or related areas: functional approximation theory, harmonic analysis, random matrix theory or information theory applied to inverse problems in speech/audio processing. Visit the lab page and see recent publications for a detailed view of ongoing projects and related works.
There are two openings one being open-ended to develop theories of deep learning while the other one is focused on explainability, bias and fairness in speech/audio applications.
Research Associate (RA):
This position is in the area of Topological Data Analysis in collaboration with IIT Delhi. The position is for a 2 year joint IIITD-IITD project on developing methods to evaluate transfer learning in deep models. Applicants will have or be close to completing, a Masters in (applied) topology, geometry or numerical linear algebra. Foundation in topics such as harmonic analysis, machine learning or deep learning is a plus. Experience in computer programming and computational mathematics is desirable. There is a possibility for the strong candidate to join as a regular PhD student.
General Eligibility (To apply in Cross-Caps Lab)
Candidates should have Bachelors/Masters in Mathematics/ECE/CSE or related disciplines. Minimum 7.5 CGPA in UG and PG degrees. Strong coding skill in Python and publications in top-tier AI/ML conferences/journals is desirable. Experience with deep learning frameworks and/or speech and audio tools such as Kaldi, Speech-Brain, TF-ASR, Festival is a plus.
---------------------------------------------------------
Dr. Vinayak Abrol
Assistant Professor, Infosys Center for AI
CSE Department, IIITD Delhi, India
Dear colleagues,
Cross-Caps Lab at IIIT Delhi, India has openings for outstanding students with an interest in speech/audio processing, machine-learning and deep-learning. To initiate discussion, please send an expression of interest email with the subject "Interest: PhD/RA with Cross-Caps" at abrol[at]iiitd.ac.in with your CV if you have relevant research experience.
Cross-Caps Lab: https://bit.ly/38UqDk1
PhD Openings:
This PhD opening is suitable for a candidate with a strong background in one of these or related areas: functional approximation theory, harmonic analysis, random matrix theory or information theory applied to inverse problems in speech/audio processing. Visit the lab page and see recent publications for a detailed view of ongoing projects and related works.
There are two openings one being open-ended to develop theories of deep learning while the other one is focused on explainability, bias and fairness in speech/audio applications.
Research Associate (RA):
This position is in the area of Topological Data Analysis in collaboration with IIT Delhi. The position is for a 2 year joint IIITD-IITD project on developing methods to evaluate transfer learning in deep models. Applicants will have or be close to completing, a Masters in (applied) topology, geometry or numerical linear algebra. Foundation in topics such as harmonic analysis, machine learning or deep learning is a plus. Experience in computer programming and computational mathematics is desirable. There is a possibility for the strong candidate to join as a regular PhD student.
General Eligibility (To apply in Cross-Caps Lab)
Candidates should have Bachelors/Masters in Mathematics/ECE/CSE or related disciplines. Minimum 7.5 CGPA in UG and PG degrees. Strong coding skill in Python and publications in top-tier AI/ML conferences/journals is desirable. Experience with deep learning frameworks and/or speech and audio tools such as Kaldi, Speech-Brain, TF-ASR, Festival is a plus.
---------------------------------------------------------
Dr. Vinayak Abrol
Assistant Professor, Infosys Center for AI
CSE Department, IIITD Delhi, India
Google
विनायक अबरोल | Vinayak Abrol - Cross-Caps Laboratory
People
1. Postdoctoral / Junior Scientist position in complex networks and information theory is available to join the Complex Networks and Brain Dynamics group for the project: “Network modelling of complex systems: from correlation graphs to information hypergraphs“ funded by the Czech Science Foundation. More information and application at https://www.cs.cas.cz/job-offer/postdoc-junior-position-Hlinka5-2021/en
2. Postdoctoral / Junior Scientist position in Multimodal Neuroimaging Machine Learning is available to join the Complex Networks and Brain Dynamics group for the project: “Predicting functional outcome in schizophrenia from multimodal neuroimaging and clinical data“ funded by the Czech Health Research Council. More information and application at https://www.cs.cas.cz/job-offer/postdoc-junior-position-Hlinka6-2021/en
Do not hesitate to contact the principal investigator for informal inquiries concerning the position: Ing. Mgr. Jaroslav Hlinka, Ph.D., [email protected].
Jaroslav Hlinka
Head of the Department of Complex Systems
Institute of Computer Science
Czech Academy of Sciences
Pod Vodarenskou vezi 2
Prague 8, 182 07, Czech Republic
Web: https://cs.cas.cz/hlinka
2. Postdoctoral / Junior Scientist position in Multimodal Neuroimaging Machine Learning is available to join the Complex Networks and Brain Dynamics group for the project: “Predicting functional outcome in schizophrenia from multimodal neuroimaging and clinical data“ funded by the Czech Health Research Council. More information and application at https://www.cs.cas.cz/job-offer/postdoc-junior-position-Hlinka6-2021/en
Do not hesitate to contact the principal investigator for informal inquiries concerning the position: Ing. Mgr. Jaroslav Hlinka, Ph.D., [email protected].
Jaroslav Hlinka
Head of the Department of Complex Systems
Institute of Computer Science
Czech Academy of Sciences
Pod Vodarenskou vezi 2
Prague 8, 182 07, Czech Republic
Web: https://cs.cas.cz/hlinka
Funded PhD in Reliable Uncertainties for Machine Learning at Ulster University, UK
Blindly trusting the predictions made by a machine learning model can lead to disaster. A more cautious approach is to consider the uncertainty in a model’s predictions (the “predictive uncertainty”), before taking any action based on them. However, just as we should not blindly trust a model’s predictions, nor should we blindly trust a model’s predictive uncertainties either, otherwise it may provide the user with nothing but a false sense of security.
Having a reliable predictive uncertainty is important in a growing number of applications. For example, electrical grid operators routinely make forecasts concerning the energy output from renewable sources, such as solar and wind. Reliably quantifying the uncertainty in these forecasts [1] enables the renewable energies to be incorporated into the grid more intelligently.
Recently, methods have been proposed which can “calibrate” the predictive uncertainty of a model [2,3] to ensure it is neither over-confident (consistently under-estimating predictive uncertainty) nor under-confident (consistently over-estimating predictive uncertainty). This PhD project, based in the AI Research Centre at Ulster University, will develop further algorithms in this important research area, optimized for a number of different high-impact applications across science and engineering that require reliable predictive uncertainty.
The exact research challenges to be addressed in this project can be tailored to the interests and experience of the PhD candidate.
The application deadline is Monday 7 February 2022
Further details, including levels of funding and how to apply, are available here: https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1045021
For informal queries on this project, please contact Dr Glenn Hawe: [email protected]
References
[1] Zelikman, E. et al. (2020). Short-term solar irradiance forecasting using calibrated probabilistic models, arXiv:2010.04715.
[2] Kuleshov, V. et al. (2018). “Accurate uncertainties for deep learning using calibrated regression,” in International Conference on Machine Learning. PMLR, 2018, pp. 2796–2804.
[3] Zelikman et al. (2020). “CRUDE: Calibrating regression uncertainty distributions empirically,” arXiv preprint arXiv:2005.12496, 2020
Blindly trusting the predictions made by a machine learning model can lead to disaster. A more cautious approach is to consider the uncertainty in a model’s predictions (the “predictive uncertainty”), before taking any action based on them. However, just as we should not blindly trust a model’s predictions, nor should we blindly trust a model’s predictive uncertainties either, otherwise it may provide the user with nothing but a false sense of security.
Having a reliable predictive uncertainty is important in a growing number of applications. For example, electrical grid operators routinely make forecasts concerning the energy output from renewable sources, such as solar and wind. Reliably quantifying the uncertainty in these forecasts [1] enables the renewable energies to be incorporated into the grid more intelligently.
Recently, methods have been proposed which can “calibrate” the predictive uncertainty of a model [2,3] to ensure it is neither over-confident (consistently under-estimating predictive uncertainty) nor under-confident (consistently over-estimating predictive uncertainty). This PhD project, based in the AI Research Centre at Ulster University, will develop further algorithms in this important research area, optimized for a number of different high-impact applications across science and engineering that require reliable predictive uncertainty.
The exact research challenges to be addressed in this project can be tailored to the interests and experience of the PhD candidate.
The application deadline is Monday 7 February 2022
Further details, including levels of funding and how to apply, are available here: https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1045021
For informal queries on this project, please contact Dr Glenn Hawe: [email protected]
References
[1] Zelikman, E. et al. (2020). Short-term solar irradiance forecasting using calibrated probabilistic models, arXiv:2010.04715.
[2] Kuleshov, V. et al. (2018). “Accurate uncertainties for deep learning using calibrated regression,” in International Conference on Machine Learning. PMLR, 2018, pp. 2796–2804.
[3] Zelikman et al. (2020). “CRUDE: Calibrating regression uncertainty distributions empirically,” arXiv preprint arXiv:2005.12496, 2020
www.ulster.ac.uk
Reliable Uncertainties for Machine Learning
2 year POSTDOC POSITION AT UNIVERSITY OF EDINBURGH
Dear all,
this is a post-doc opportunity on integrating causality and knowledge graphs for misinformation.
Project title: Causal Knowledge Graphs for Reasoning about Counterfactual Claims
The School of Informatics, University of Edinburgh invites applications for a post-doctoral Research Associate position under the supervision of Dr Björn Ross and Dr Vaishak Belle. The postholder will be part of the new Edinburgh Laboratory for Integrated Artificial Intelligence, the SMASH group and the Belle Lab.
Current AI approaches to misinformation detection often learn to recognise paraphrases of previously seen claims. Detecting new misinformation is much harder, and linguistic cues are not enough to distinguish fact from fiction. Our approach is grounded in knowledge graphs and the logic of causality. However, this approach has its own challenges. Much of the misinformation encountered is not limited to simple factual statements that can be tested against a structured representation of knowledge but it consists of more complex claims such as counterfactual statements. To address this problem, we integrate approaches from different subfields of computer science, namely, computational logic, deep learning and natural language processing.
The position is for 24 months. The post closes at 5pm UK time on 23 January 2022.
Grade UE07, salary range: £34,304 - £40,92
Access application at https://twitter.com/bjoernross/status/1469273157156618256
Dear all,
this is a post-doc opportunity on integrating causality and knowledge graphs for misinformation.
Project title: Causal Knowledge Graphs for Reasoning about Counterfactual Claims
The School of Informatics, University of Edinburgh invites applications for a post-doctoral Research Associate position under the supervision of Dr Björn Ross and Dr Vaishak Belle. The postholder will be part of the new Edinburgh Laboratory for Integrated Artificial Intelligence, the SMASH group and the Belle Lab.
Current AI approaches to misinformation detection often learn to recognise paraphrases of previously seen claims. Detecting new misinformation is much harder, and linguistic cues are not enough to distinguish fact from fiction. Our approach is grounded in knowledge graphs and the logic of causality. However, this approach has its own challenges. Much of the misinformation encountered is not limited to simple factual statements that can be tested against a structured representation of knowledge but it consists of more complex claims such as counterfactual statements. To address this problem, we integrate approaches from different subfields of computer science, namely, computational logic, deep learning and natural language processing.
The position is for 24 months. The post closes at 5pm UK time on 23 January 2022.
Grade UE07, salary range: £34,304 - £40,92
Access application at https://twitter.com/bjoernross/status/1469273157156618256
Twitter
Björn Ross
Looking for a post-doctoral Research Associate at @InfAtEd. Come work with @vaishakbelle and me if you’re interested in misinformation, #nlproc and computational logic! The post is for 24 months, deadline 23 Jan. #AcademicTwitter #hiring #postdoc elxw.fa…
2 PhD positions in Artificial Intelligence (AI) for sustainable manufacturing
The Department of Data Science and Knowledge Engineering (DKE) at Maastricht University, the Netherlands, is looking for 2 PhD candidates in AI for sustainable manufacturing.
In a joint collaboration with VDL Nedcar, the largest Dutch automotive manufacturing company, you will work in a team to investigate AI techniques within VDL Nedcar’s manufacturing environment. The ultimate goal is to make intelligent decisions in a transparent and reliable way, reduce costs, and save energy and reduce overall CO2 emissions. The developed AI technology will be embedded in a Digital Twin: a real-time simulation of VDL Nedcar’s battery line for the production of electronic vehicles.
As a PhD candidate, you will primarily address the following four topics: (1) planning & scheduling (2) prescriptive quality (3) predictive maintenance and (4) hybrid intelligence.
These PhD positions are part of the Green Transport Delta, a public-private innovation programme (funded by the Dutch Ministry of Economic Affairs and Climate) that aims to make Dutch transport sectors futureproof and sustainable. You will be embedded in the consortium around electrification, which focuses on improving various aspects of battery-powered electric transport as a key component of the transition to climate-neutral mobility.
You will be expected to:
- perform scientific research in AI for sustainable manufacturing as described above;
- publish results at (international) conferences and in international journals;
- collaborate with other group and faculty members;
- assist with educational tasks (e.g. assist in courses, supervise Bachelor/Master students).
Requirements
- MSc degree in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics;
- Strong programming skills;
- Proficiency in English (oral and written);
- Excellent communication skills;
- Ability to collaborate in an international setting.
**** CONDITIONS OF EMPLOYMENT
Fixed-term contract: 4 years.
We offer a 1.0 fte contract for a period of 4 years, starting preferably as soon as possible. Continuation after the first year is dependent upon a positive evaluation.
The salary will be set in PhD salary scale of the Collective Labour Agreement of the Dutch Universities (€2.434 gross per month in first year to €3.111 in the fourth and final year). On top of this, there is an 8% holiday and an 8.3% year-end allowance. The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO). Furthermore, local UM provisions also apply. Non-Dutch applicants could be eligible for a favorable tax treatment (30% rule).
**** ORGANIZATION
Maastricht University. Maastricht University (UM) has around 20,000 students and 4,700 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. UM placed #6 in Times Higher Education’s (THE) Young Universities Ranking 2021, and #127 in THE’s World University Rankings 2022.
https://www.maastrichtuniversity.nl
**** DEPARTMENT
The Department of Data Science and Knowledge Engineering. Founded in 1992, we are a fast-growing department undertaking internationally respected research in the areas of computer science, artificial intelligence, data science, robotics and applied mathematics. Much of our research takes place at the interfaces of these disciplines. We maintain a large network of industry partners and provide education through one bachelor’s programme and two master’s programmes.
The Department of Data Science and Knowledge Engineering (DKE) at Maastricht University, the Netherlands, is looking for 2 PhD candidates in AI for sustainable manufacturing.
In a joint collaboration with VDL Nedcar, the largest Dutch automotive manufacturing company, you will work in a team to investigate AI techniques within VDL Nedcar’s manufacturing environment. The ultimate goal is to make intelligent decisions in a transparent and reliable way, reduce costs, and save energy and reduce overall CO2 emissions. The developed AI technology will be embedded in a Digital Twin: a real-time simulation of VDL Nedcar’s battery line for the production of electronic vehicles.
As a PhD candidate, you will primarily address the following four topics: (1) planning & scheduling (2) prescriptive quality (3) predictive maintenance and (4) hybrid intelligence.
These PhD positions are part of the Green Transport Delta, a public-private innovation programme (funded by the Dutch Ministry of Economic Affairs and Climate) that aims to make Dutch transport sectors futureproof and sustainable. You will be embedded in the consortium around electrification, which focuses on improving various aspects of battery-powered electric transport as a key component of the transition to climate-neutral mobility.
You will be expected to:
- perform scientific research in AI for sustainable manufacturing as described above;
- publish results at (international) conferences and in international journals;
- collaborate with other group and faculty members;
- assist with educational tasks (e.g. assist in courses, supervise Bachelor/Master students).
Requirements
- MSc degree in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics;
- Strong programming skills;
- Proficiency in English (oral and written);
- Excellent communication skills;
- Ability to collaborate in an international setting.
**** CONDITIONS OF EMPLOYMENT
Fixed-term contract: 4 years.
We offer a 1.0 fte contract for a period of 4 years, starting preferably as soon as possible. Continuation after the first year is dependent upon a positive evaluation.
The salary will be set in PhD salary scale of the Collective Labour Agreement of the Dutch Universities (€2.434 gross per month in first year to €3.111 in the fourth and final year). On top of this, there is an 8% holiday and an 8.3% year-end allowance. The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO). Furthermore, local UM provisions also apply. Non-Dutch applicants could be eligible for a favorable tax treatment (30% rule).
**** ORGANIZATION
Maastricht University. Maastricht University (UM) has around 20,000 students and 4,700 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. UM placed #6 in Times Higher Education’s (THE) Young Universities Ranking 2021, and #127 in THE’s World University Rankings 2022.
https://www.maastrichtuniversity.nl
**** DEPARTMENT
The Department of Data Science and Knowledge Engineering. Founded in 1992, we are a fast-growing department undertaking internationally respected research in the areas of computer science, artificial intelligence, data science, robotics and applied mathematics. Much of our research takes place at the interfaces of these disciplines. We maintain a large network of industry partners and provide education through one bachelor’s programme and two master’s programmes.
Our new colleague(s) will be joining a tight-knit department consisting of ~70 principal investigators, postdocs and PhD students, 800 BSc and MSc students and a team of dedicated support staff members. Together, we come from over 40 different countries.
https://www.maastrichtuniversity.nl/dke
The Faculty of Science and Engineering. Maastricht University heavily invests in the growth of its STEM research and education. The Faculty of Science and Engineering – which houses the Department of Data Science and Knowledge Engineering - is one of the focal points of these developments. Within the Faculty of Science and Engineering, over 260 researchers and more than 2,700 students work on themes such as fundamental physics, circularity and sustainability, data science and artificial intelligence.
https://www.maastrichtuniversity.nl/fse
**** HOW TO APPLY
Applicants are asked to prepare an application consisting of:
Your application must contain the following documents (all in English):
- cover letter (1 page max), which includes a motivation of your interest in the vacancy and an explanation of why you would fit well for the PhD position;
- a detailed curriculum vitae;
- a course list of your Masters and Bachelor programs (including grades);
- results of a recent English language test, or other evidence of your English language capabilities;
- name and contact information of two references
Applications received by January 5, 2022 will receive full consideration. Applicants will be called in for an (online) interview. We intend to fill this position as soon as possible; the starting date for this position is early, 2022.
Applications for these positions can be directed to:
https://www.academictransfer.com/en/307396/2-phd-positions-in-artificial-intelligence-ai-for-sustainable-manufacturing/apply/#apply
https://www.maastrichtuniversity.nl/dke
The Faculty of Science and Engineering. Maastricht University heavily invests in the growth of its STEM research and education. The Faculty of Science and Engineering – which houses the Department of Data Science and Knowledge Engineering - is one of the focal points of these developments. Within the Faculty of Science and Engineering, over 260 researchers and more than 2,700 students work on themes such as fundamental physics, circularity and sustainability, data science and artificial intelligence.
https://www.maastrichtuniversity.nl/fse
**** HOW TO APPLY
Applicants are asked to prepare an application consisting of:
Your application must contain the following documents (all in English):
- cover letter (1 page max), which includes a motivation of your interest in the vacancy and an explanation of why you would fit well for the PhD position;
- a detailed curriculum vitae;
- a course list of your Masters and Bachelor programs (including grades);
- results of a recent English language test, or other evidence of your English language capabilities;
- name and contact information of two references
Applications received by January 5, 2022 will receive full consideration. Applicants will be called in for an (online) interview. We intend to fill this position as soon as possible; the starting date for this position is early, 2022.
Applications for these positions can be directed to:
https://www.academictransfer.com/en/307396/2-phd-positions-in-artificial-intelligence-ai-for-sustainable-manufacturing/apply/#apply
www.maastrichtuniversity.nl
Department of Advanced Computing Sciences
Research Associate/Fellow in ML for Computational Biology at University of Glasgow
The School of Computing Science, University of Glasgow (https://www.gla.ac.uk/computing) is looking for a postdoc to work on a joint project with the Human Genetics Group of the Global Computational Biology and Digital Science (gCBDS) area at Boehringer Ingelheim (BI, https://www.boehringer-ingelheim.com/).
Leveraging rich data from the human biobanks such as the UK Biobank and The Cancer Genome Atlas (TCGA), the postholder will be working in the broad area of deep learning for medical image, omics and genetic data. We are looking for someone with experience / wish to learn the following areas: deep representation learning, medical image, genetic and clinical data analysis.
Apply here: https://my.corehr.com/pls/uogrecruit/erq_jobspec_version_4.jobspec?p_id=075127
Deadline: 13 January 2022
Informal enquiries and requests for further information can be made to Dr. Ke Yuan (e-mail: [email protected]).
The School of Computing Science, University of Glasgow (https://www.gla.ac.uk/computing) is looking for a postdoc to work on a joint project with the Human Genetics Group of the Global Computational Biology and Digital Science (gCBDS) area at Boehringer Ingelheim (BI, https://www.boehringer-ingelheim.com/).
Leveraging rich data from the human biobanks such as the UK Biobank and The Cancer Genome Atlas (TCGA), the postholder will be working in the broad area of deep learning for medical image, omics and genetic data. We are looking for someone with experience / wish to learn the following areas: deep representation learning, medical image, genetic and clinical data analysis.
Apply here: https://my.corehr.com/pls/uogrecruit/erq_jobspec_version_4.jobspec?p_id=075127
Deadline: 13 January 2022
Informal enquiries and requests for further information can be made to Dr. Ke Yuan (e-mail: [email protected]).
www.gla.ac.uk
University of Glasgow - Schools - School of Computing Science
Computer Science, Computing Science, University of Glasgow
4 PhD positions at TU Delft
The newly formed BIO lab (https://www.tudelft.nl/ai/biolab) at the Delft University of Technology (https://www.tudelft.nl) in the Netherlands is looking for 4 fully funded PhD students:
1. "Sample efficient reinforcement learning in neuroscience"
(https://www.academictransfer.com/nl/307448/biolab-phd-position-14-sample-efficient-reinforcement-learning-in-neuroscience)
2. "Efficient learning of neural tissue models"
(https://www.academictransfer.com/nl/307445/biolab-phd-position-24-efficient-learning-of-neural-tissue-models)
3. "Generative and reinforcement learning methods for cancer treatment"
(https://www.academictransfer.com/en/307438/biolab-phd-position-34-generative-and-reinforcement-learning-methods-for-cancer-treatment)
4. "Deep learning and smart super-resolution microscopy"
(https://www.academictransfer.com/en/307437/biolab-phd-position-44-deep-learning-and-smart-super-resolution-microscopy)
Candidates will work on cutting-edge research at the intersection of AI and biomedical/neuro-science!
The newly formed BIO lab (https://www.tudelft.nl/ai/biolab) at the Delft University of Technology (https://www.tudelft.nl) in the Netherlands is looking for 4 fully funded PhD students:
1. "Sample efficient reinforcement learning in neuroscience"
(https://www.academictransfer.com/nl/307448/biolab-phd-position-14-sample-efficient-reinforcement-learning-in-neuroscience)
2. "Efficient learning of neural tissue models"
(https://www.academictransfer.com/nl/307445/biolab-phd-position-24-efficient-learning-of-neural-tissue-models)
3. "Generative and reinforcement learning methods for cancer treatment"
(https://www.academictransfer.com/en/307438/biolab-phd-position-34-generative-and-reinforcement-learning-methods-for-cancer-treatment)
4. "Deep learning and smart super-resolution microscopy"
(https://www.academictransfer.com/en/307437/biolab-phd-position-44-deep-learning-and-smart-super-resolution-microscopy)
Candidates will work on cutting-edge research at the intersection of AI and biomedical/neuro-science!
TU Delft
BIOLab
PhD and Research Associate positions available at University of Edinburgh in Machine Learning and Bioinformatics
Dear all,
We have two openings for a PhD position (Precision Medicine DTP) and a research associate position (Shankar-Hari group) at the University of Edinburgh. Both positions are related to machine learning and bioinformatics for characterising and stratifying the immune response for critically ill patients. We will be analysing multi-modal measurements to better understand the immune networks of critical illness and use explainable machine learning methods to stratify patients in interpretable groups to better capture their clinical and biological variability. More information can be found at PhD position and Research Associate position. Please drop me an email if you want to know more about the positions.
Best, Sohan
Dear all,
We have two openings for a PhD position (Precision Medicine DTP) and a research associate position (Shankar-Hari group) at the University of Edinburgh. Both positions are related to machine learning and bioinformatics for characterising and stratifying the immune response for critically ill patients. We will be analysing multi-modal measurements to better understand the immune networks of critical illness and use explainable machine learning methods to stratify patients in interpretable groups to better capture their clinical and biological variability. More information can be found at PhD position and Research Associate position. Please drop me an email if you want to know more about the positions.
Best, Sohan
MBZUAI postdoc positions
There are one to two positions, in the group of Zhiqiang Xu from Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE, for postdoc doing research on one or more of the following topics: deep learning, graph learning, reinforcement learning, optimization, statistics, etc. Successful applicants are expected to have a strong publication record, and be able to work independently and produce high-quality research outputs. There will be a high degree of freedom and ample computing resources for postdocs to conduct the research. Salary and benefit are 60k-100k USD dollars per year (tax free) and annual round trip air tickets home.
Anyone interested feels free to drop him an email at [email protected] with CV attached. (Please don't send emails to me)
There are one to two positions, in the group of Zhiqiang Xu from Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE, for postdoc doing research on one or more of the following topics: deep learning, graph learning, reinforcement learning, optimization, statistics, etc. Successful applicants are expected to have a strong publication record, and be able to work independently and produce high-quality research outputs. There will be a high degree of freedom and ample computing resources for postdocs to conduct the research. Salary and benefit are 60k-100k USD dollars per year (tax free) and annual round trip air tickets home.
Anyone interested feels free to drop him an email at [email protected] with CV attached. (Please don't send emails to me)
A fully funded PhD opportunity– Ulster University, Belfast, UK: Knowledge Enhanced Imbalanced Learning (KEIL)
Summary: There is currently a great deal of interest in applying data analytic to real world problems characterized by imbalanced data, i.e., imbalanced learning, which are concerned across a wide range of research and application areas. For example, rare event detection, as these events occur with low frequency in daily life, but may cause far-reaching impact, including natural disaster, hazards and risks in finance and industry, and diseases. Although many methods have been proposed, there are still some key limitations. One limitation is that the learning performance is still relative low. Another limitation is the lack of an ability with most of machine learning system to explain its outputs, which has fuelled recent research in explainable AI.
This project will study knowledge-enhanced imbalanced learning, i.e., both knowledge and data are used in the process of learning, and how to structure relevant and reliable knowledge and incorporate them within the roadmap of imbalanced data analytic. The knowledge may be problem context, principles, guidelines, expert experience, or characterisation of objects. On the one hand, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully to enhance the learning performance. On the other hand, it is expected that a knowledge-enhanced learning system will have innate capabilities for explanation and interpretability.
This project provides an opportunity to combine cutting edge research at the intersection of knowledge and machine learning to address the above key challenges.
The timeliness of this PhD project becomes also apparent in the potential of the above integration to contribute to the long-standing goal of explainable and interpretable AI in emerging real world applications. This project will investigate fundamental research questions about knowledge-enhanced imbalanced learning and will be guided by various application scenarios where rich domain knowledge exists, such as human activity recognition, telematic data analytics, risk/safety assessment, or medical decision making.
Applicants can find further details, including shortlisting essential and desirable criteria, funding, eligibility criteria and levels of support by visiting https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1045082. For further details about the project, please contact Dr. Jun Liu (phone: +44 28 9536 5687, E-mail: [email protected]).
The application deadline is Monday 7 February 2022
Recommended reading
· H.X. Guo et al. (2017), Learning from class-imbalanced data: review of methods and applications, Expert Systems with Applications, DOI: 10.1016/j.eswa.2016.12.035.
· Z. Chen, et al. (2021), A hybrid data-level ensemble to enable learning from highly imbalanced dataset, Information Sciences. DOI: 10.1016/j.ins.2020.12.023.
· J. Liu, L. Martínez, A. Calzada, and H. Wang (2013), A novel belief rule base representation, generation and its inference methodology, Knowledge-Based Systems. DOI: 10.1016/j.knosys.2013.08.019.
· L.H. Yang, J. Liu, Y.M. Wang, and L. Martínez (2018), A micro-extended belief rule-based system for big data multi-class classification problems, IEEE Transactions on Systems, Man, and Cybernetics: Systems. DOI: 10.1109/TSMC.2018.2872843.
· L.H. Yang, J. Liu, F.F. Ye, Y.M. Wang, C. Nugent, H. Wang, and L. Martínez (2021), Highly explainable cumulative belief rule-based system with effective rule-base modelling and inference scheme, Knowledge-Based Systems, accepted and in press.
· L.H. Yang, J. Liu, Y.M. Wang, C. Nugent, and L. Martínez (2021), Online updating extended belief rule-based system for sensor-based activity recognition expert systems with applications, Expert Systems with Applications. DOI: 10.1016/j.eswa.2021.115737.
Summary: There is currently a great deal of interest in applying data analytic to real world problems characterized by imbalanced data, i.e., imbalanced learning, which are concerned across a wide range of research and application areas. For example, rare event detection, as these events occur with low frequency in daily life, but may cause far-reaching impact, including natural disaster, hazards and risks in finance and industry, and diseases. Although many methods have been proposed, there are still some key limitations. One limitation is that the learning performance is still relative low. Another limitation is the lack of an ability with most of machine learning system to explain its outputs, which has fuelled recent research in explainable AI.
This project will study knowledge-enhanced imbalanced learning, i.e., both knowledge and data are used in the process of learning, and how to structure relevant and reliable knowledge and incorporate them within the roadmap of imbalanced data analytic. The knowledge may be problem context, principles, guidelines, expert experience, or characterisation of objects. On the one hand, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully to enhance the learning performance. On the other hand, it is expected that a knowledge-enhanced learning system will have innate capabilities for explanation and interpretability.
This project provides an opportunity to combine cutting edge research at the intersection of knowledge and machine learning to address the above key challenges.
The timeliness of this PhD project becomes also apparent in the potential of the above integration to contribute to the long-standing goal of explainable and interpretable AI in emerging real world applications. This project will investigate fundamental research questions about knowledge-enhanced imbalanced learning and will be guided by various application scenarios where rich domain knowledge exists, such as human activity recognition, telematic data analytics, risk/safety assessment, or medical decision making.
Applicants can find further details, including shortlisting essential and desirable criteria, funding, eligibility criteria and levels of support by visiting https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1045082. For further details about the project, please contact Dr. Jun Liu (phone: +44 28 9536 5687, E-mail: [email protected]).
The application deadline is Monday 7 February 2022
Recommended reading
· H.X. Guo et al. (2017), Learning from class-imbalanced data: review of methods and applications, Expert Systems with Applications, DOI: 10.1016/j.eswa.2016.12.035.
· Z. Chen, et al. (2021), A hybrid data-level ensemble to enable learning from highly imbalanced dataset, Information Sciences. DOI: 10.1016/j.ins.2020.12.023.
· J. Liu, L. Martínez, A. Calzada, and H. Wang (2013), A novel belief rule base representation, generation and its inference methodology, Knowledge-Based Systems. DOI: 10.1016/j.knosys.2013.08.019.
· L.H. Yang, J. Liu, Y.M. Wang, and L. Martínez (2018), A micro-extended belief rule-based system for big data multi-class classification problems, IEEE Transactions on Systems, Man, and Cybernetics: Systems. DOI: 10.1109/TSMC.2018.2872843.
· L.H. Yang, J. Liu, F.F. Ye, Y.M. Wang, C. Nugent, H. Wang, and L. Martínez (2021), Highly explainable cumulative belief rule-based system with effective rule-base modelling and inference scheme, Knowledge-Based Systems, accepted and in press.
· L.H. Yang, J. Liu, Y.M. Wang, C. Nugent, and L. Martínez (2021), Online updating extended belief rule-based system for sensor-based activity recognition expert systems with applications, Expert Systems with Applications. DOI: 10.1016/j.eswa.2021.115737.
www.ulster.ac.uk
Knowledge Enhanced Imbalanced Learning (KEIL)
The Cologne Graduate School in Management, Economics, and Social Sciences (CGS) offers one Doctoral Scholarship in Business Administration (three years, starting October 1, 2022).
The Cologne Graduate School in Management, Economics and Social Sciences (CGS) at the University of Cologne (UoC) offers one three-year doctoral scholarship to outstanding students holding a Master’s degree (or equivalent) in Business Administration, Economics, Computer Science, Information Systems or Statistics.
The scholarship is integrated into the newly created initiative Analytics and Transformation. This initiative brings together researchers from different disciplines to work on research projects at the intersection of analytics, artificial intelligence, entrepreneurship, and innovation. Within the initiative we cover substantive topics that include marketing analytics, health, digital markets, digital innovation, digital transformation, and others.
The scholarship holder will be working closely with the principal researchers and will have the opportunity to participate in research seminars, workshops, and soft-skill courses. Moreover, they will be part of a vibrant and international research network centered around the Marketing, Information Systems, Operations, and Corporate Development.
About the Program
The scholarship holder will be enrolled in the Management doctoral track of the CGS of the Faculty of Management, Economics and Social Sciences (WiSo). The course program will start in October 2022. During the first year, students will take part in courses on multidisciplinary methods and theories, as well as subject-specific courses. Courses are taught in English. During the second and third year, students mainly conduct research and work on their thesis.
Qualifications
We are inviting applications by highly qualified graduates from Business Administration, Economics, Computer Science, Information Systems or Statistics. Candidates must hold a Master’s degree (or equivalent) or be very close to completion. We are looking for individuals who have demonstrated ability to work with business and social science data and who are confident in working with statistical software (e.g., R) and programming for analytics (e.g., Python, in particular with libraries such as scikit-learn, pandas, Keras, NumPy, or pyTorch).
Scholarships
The scholarship amounts to 1,365€ per month plus a yearly research budget of 1,000€. The scholarship is awarded for a maximum period of three years and is tax-free.
Application Procedure
Application is online only. The deadline for the submission of your application is March 1, 2022. For further information about the CGS please visit the School’s website. For more information on the application process and application documents, please visit the section on scholarship application.
Contact:
Dr. Katharina Laske ([email protected])
Best regards,
Christoph
The Cologne Graduate School in Management, Economics and Social Sciences (CGS) at the University of Cologne (UoC) offers one three-year doctoral scholarship to outstanding students holding a Master’s degree (or equivalent) in Business Administration, Economics, Computer Science, Information Systems or Statistics.
The scholarship is integrated into the newly created initiative Analytics and Transformation. This initiative brings together researchers from different disciplines to work on research projects at the intersection of analytics, artificial intelligence, entrepreneurship, and innovation. Within the initiative we cover substantive topics that include marketing analytics, health, digital markets, digital innovation, digital transformation, and others.
The scholarship holder will be working closely with the principal researchers and will have the opportunity to participate in research seminars, workshops, and soft-skill courses. Moreover, they will be part of a vibrant and international research network centered around the Marketing, Information Systems, Operations, and Corporate Development.
About the Program
The scholarship holder will be enrolled in the Management doctoral track of the CGS of the Faculty of Management, Economics and Social Sciences (WiSo). The course program will start in October 2022. During the first year, students will take part in courses on multidisciplinary methods and theories, as well as subject-specific courses. Courses are taught in English. During the second and third year, students mainly conduct research and work on their thesis.
Qualifications
We are inviting applications by highly qualified graduates from Business Administration, Economics, Computer Science, Information Systems or Statistics. Candidates must hold a Master’s degree (or equivalent) or be very close to completion. We are looking for individuals who have demonstrated ability to work with business and social science data and who are confident in working with statistical software (e.g., R) and programming for analytics (e.g., Python, in particular with libraries such as scikit-learn, pandas, Keras, NumPy, or pyTorch).
Scholarships
The scholarship amounts to 1,365€ per month plus a yearly research budget of 1,000€. The scholarship is awarded for a maximum period of three years and is tax-free.
Application Procedure
Application is online only. The deadline for the submission of your application is March 1, 2022. For further information about the CGS please visit the School’s website. For more information on the application process and application documents, please visit the section on scholarship application.
Contact:
Dr. Katharina Laske ([email protected])
Best regards,
Christoph
Postdoctoral Research Associate in Machine Learning for Medical Image Analysis at University of Sheffield
for Medical Image Analysis to join our team at University of Sheffield, with a fixed term till 31st March 2023 and a start date as soon as possible.
See details and apply at Data Overview: Job Posting (shef.ac.uk)
Deadline: 12th Jan 2022.
Haiping Lu
--
Senior Lecturer in Machine Learning
Department of Computer Science
University of Sheffield
for Medical Image Analysis to join our team at University of Sheffield, with a fixed term till 31st March 2023 and a start date as soon as possible.
See details and apply at Data Overview: Job Posting (shef.ac.uk)
Deadline: 12th Jan 2022.
Haiping Lu
--
Senior Lecturer in Machine Learning
Department of Computer Science
University of Sheffield