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/

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

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

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

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

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.

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

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]).

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!

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

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)

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.

Doctoral Scholarship for Machine Learning in Business Administration

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

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

Optical Biology PhD Program at UCL (deadline extended to 20 December)
We invite applications for the Optical Biology 4-year PhD program at UCL.



This program brings together neuroscientists, cell biologists, physicists, chemists and computational scientists at UCL, with world-leading industrial and academic partners, to deliver an integrated training programme in the most advanced optical methods and analysis tools. The program offers a strong focus on bespoke personal mentorship and career development support for students.



Full funding will be available for top-ranked applicants; research expenses will also be provided, as well as funds to attend international courses and meetings. We also provide transition costs at the end of the PhD to help you move to the next stage of your career.



The EXTENDED application deadline is Monday 20 December 2022.



For more details and information about applying please visit:



https://opticalbiology.org



Applications and queries should be sent to:



[email protected]





—

Michael Hausser

Director, Optical Biology PhD Program

Facilitator, International Brain Laboratory

Professor of Neuroscience, UCL



tel +44-20-7679-6756

email [email protected]

Postdoc position for 2 years in Aarhus University (Denmark) to work on the gut-brain axis
Hello,

I am hiring a postdoc for my new lab for a 2 years position in beautiful Aarhus. Come join me in studying the enteric nervous system of Zebrafish in a brand new building and lab. I you're interested in light-sheet microscopy, the enteric nervous system, gut-brain axis or the microbiome don't hesitate to get in touch.

More information, and how to apply can be found on https://mbg.au.dk/en/news-and-events/vacancies/job/postdoc-position-in-neurobiology

Kind regards,
Gilles Vanwalleghem
Assistant Professor in Neurobiology
Department of Molecular Biology and Genetics
Aarhus University
Denmark

Ph.D. positions in Reinforcement Learning at TU Darmstadt
The LiteRL group, led by Dr. Carlo D'Eramo, funded by Hessian.AI, and located at TU Darmstadt, is seeking 2 Ph.D. students with a great interest in the highly interdisciplinary field of Reinforcement Learning. The LiteRL group will research lightweight methods for (deep) Reinforcement Learning to enhance autonomy and adaptation of agents.

All students with a passion for #AI and #MachineLearning are strongly encouraged to apply.

Complete job post at https://bit.ly/3lMVgP1

For any questions, drop me an e-mail!

Cheers!

Postdoc position social networks/loneliness in early adulthood
We are now inviting applications for a postdoctoral position at the Center for Social and Affective Neuroscience (CSAN), Linköping University, Sweden. The successful applicant will be mainly working on a project relating to social networks and loneliness in late adolescence/early adulthood. Specifically, the project will investigate social adjustments occurring when transitioning from a high school to a university. The project will focus on the general population and utilise multiple different data collection methods including ecological momentary assessment, fMRI and fEMG.

The applicant doesn’t need to be an expert in all of these methods, but they should preferably have experience in at least one of the above and be willing to learn the other ones. Strong statistical skills and experience in data analysis using R, Python, or Matlab are desirable. The successful candidate will have the ability to adapt the focus of the project to suit their own interests.

Duration: 2 years (salaried position)
Starting date: as soon as possible
Application deadline: January 3, 2022
Working language: English
Location: The Embodied Brain Lab (https://liu.se/en/research/csan/labs/morrison-lab), located in the Center for Social and Affective Neuroscience (https://liu.se/en/research/csan) at Linköping University, Linköping, Sweden

The full job ad, including instructions on how to apply, is available at https://liu.se/en/work-at-liu/vacancies?rmpage=job&rmjob=17644&rmlang=UK

Interested candidates can also contact India Morrison ([email protected]) or Juulia Suvilehto ([email protected]) for more information.

PhD and postdoctoral positions at LTSI INSERM Université de Rennes 1 in Virtual Reality, Surgical Training and Machine learning
Apply to a research project on "New digital forms for medical and surgical teaching"

The AIR project from the University of Rennes 1, in collaboration with University of Rennes 2, Inria, INSA and industrial IT companies, is one of the few national funded research projects to study new digital forms for teaching. Specifically, the AIR project aims to develop innovative operational solutions to increase and enrich pedagogical interactions through digital means. Within this project, the MediCIS/LTSI team aims 1) to develop innovative virtual reality based simulators to help learning non-technical medical and surgical skills, 2) to study data driven approaches for quantitative and objective assessment of skills based on machine learning and multimodal sensors, and 3) to promote the usage of and evaluate the developed systems and tools in medical contexts within the simulation center of the university and the collaborating medical simulation and training centers.

For this project, we are looking for one postdoc or research engineer and one PhD student in the area of virtual reality and artificial intelligence in medical and surgical training. The project will last three years. The MediCIS/LTSI lab is located within the medical university and is composed of researchers both from engineering and medicine working together on societal high value projects, in a context of responsible research, aware of social, environmental and ethical impacts.

Please contact [email protected] and [email protected] for more information (including CV and letter of motivation)

Pierre JANNIN
https://medicis.univ-rennes1.fr/
https://www.ltsi.univ-rennes1.fr/

LTSI, Inserm UMR 1099 - Université de Rennes 1
Equipe MediCIS
Faculté de Médecine 2, Avenue du Pr. Léon Bernard
35043 Rennes Cedex, France

Ph: +33 2 23 23 45 88
Fx: +33 2 23 23 69 17

Research Associate (Postdoc) position in autonomous driving, explainable AI, natural language processing

The successful candidate will join a team of interdisciplinary informatics researchers (AI planning and prediction, natural language processing, human cognitive modelling) in the School of Informatics, University of Edinburgh.

The goal of this project is to develop a system which enables human passengers to ask autonomous vehicles to explain their decisions (e.g. "Car, why did you change lanes just now? Why did you enter the junction while that other car was approaching?"). The project will build on the Interpretable Goal-based Prediction and Planning (IGP2) system published at ICRA'21 (see https://www.five.ai/igp2 for paper and videos) and develop new reasoning/explanation/NLP modules.

This position can start immediately and will have a duration of 1 year from the start of the position. International candidates are eligible. We are aiming to get additional funding to extend the position beyond 1 year.

For further details and how to apply, see here: https://elxw.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/2860/?utm_medium=jobshare

Application deadline: 5pm (UK time) on 10 January 2022

Enquiries about this position can be sent to Dr. Stefano Albrecht (https://agents.inf.ed.ac.uk/stefano-albrecht/).
--
Dr. Stefano V. Albrecht
Assistant Professor, School of Informatics, University of Edinburgh
Head of Autonomous Agents Research Group (https://agents.inf.ed.ac.uk)
Royal Society Industry Fellow, Five AI (https://www.five.ai)
Twitter: @UoE_Agents
The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.