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.

UK postdoc+phd opportunities in Trustworthy Machine Learning / Safe+Ethical AI at Cambridge and the Turing Institute
Come and join us!



Cambridge postdoc in trustworthy ML. Join the machine learning group at the University of Cambridge in the UK to work on Trustworthy Machine Learning, with a focus on Fairness, Interpretability or Robustness. We’re building foundational technical theory and methods, and working with partners in specific application settings in domains including healthcare and criminal justice.
Apply by 31 Dec https://www.jobs.cam.ac.uk/job/32684/



Turing postdocs in trustworthy, scalable, safe and ethical AI (RA or SRA level). Apply by Jan 3 https://cezanneondemand.intervieweb.it/turing/jobs/research_associatesenior_research_associate_for_safe__ethical_ai_18296/en/


Cambridge PhD. Although we’re past the cutoff date for some funding, it is still possible to apply, especially if you’re a strong candidate interested in scalable, trustworthy ML! https://mlg.eng.cam.ac.uk/?page_id=659


One area of interest is trying to ensure good performance not only of the AI system, but human-machine teams. We are always looking to connect with collaborators and stakeholders broadly. Please be in touch if interested.



Best wishes,

Adrian

--------------------------------------------------

Adrian Weller

https://mlg.eng.cam.ac.uk/adrian/

POST-DOC RESEARCHER in unsupervised statistical learning (M/F)
The offer is also available here : https://bit.ly/3s1fB6X
How to apply : https://bit.ly/3s1fB6X
Available in Paris (February 2022), France, 18 months

In general, multiview unsupervised learning provides superior results to single view and/or modality learning. These data are often heterogeneous and show significant divergence. Beyond multi-view learning, algorithms have recently been proposed to augment the properties of existing scalable clustering algorithms:

-Co-clustering, clustering individuals AND the variables used to describe them;
Probabilistic Clustering;
-Multi-clustering, aiming at performing a multi-view clustering while refining the membership of the variables to the various views;
-Topological Clustering, aiming at learning the clusters while organizing them on a low-dimensional map;
-Cell-based clustering, where the data atom is not necessarily a scalar but can be extended to a more complex structure;
-Transfer Clustering, where the result of a Clustering can be used to speed up and reduce the data requirement of a subsequent Clustering.

Although relevant and interesting separately, the properties of the above-mentioned algorithms would benefit from being combined into a general multi-view algorithm in order to take advantage of the most complete and explicit results possible with less effort.

The objective of this research project is to propose a general algorithm that combines as many of the properties mentioned above as possible, in order to limit the effort required for one person to perform reliable, topological Multi-Co-clustering with rich and explicit results (with high added value for this person) on very different datasets with multiple views / representations.
In view of the current state of the art in scalable co-clustering, we propose to address the fundamental dimension along two main axes: the first one aims at clustering the properties of cell-based probabilistic topological multi-view multi-clustering on multi-representation data, with the support of work recently done in collaboration with the LIPN; the second one aims at addressing the problem of transfer learning in the multi-view context.

**Activities

-Participate in research projects, within LIPN and in collaboration with the start-up HephIA, at the national and international levels, and in associated publications.
-Guide the choice of relevant algorithms and tools according to the problem at hand.
-Design algorithms for scalable co-clustering and visual restitution of results following the standards of the start-up HephIA.

**Skills

PhD in statistical learning or computer science (data science).
Strong background in statistics (mixture model) and computer science.
Programming skills in Python, Java/Scala
Experience with Git, Docker, Cloud environments, distributed computing on clusters
Experience in software engineering
Ability to synthesize.
Creativity, strength of proposal.

**Work Context

The post-doc researcher will join the A3 team: Artificial Learning & Applications (A3) of LIPN. The A3 team deals with problems related to machine learning and covers a broad scope of issues, including supervised and unsupervised learning and reinforcement learning. Its research is based on a variety of applications in the field of pattern recognition and data mining. The A3 team develops fundamental research while intensifying its cooperation with large organizations and industry.

This recruitment is part of the AMIES funding in collaboration with the start-up HephIA (https://hephia.com/).

Best regards
Mustapha Lebbah
------------------------------------------------------------------
Université Sorbonne Paris Nord,
Laboratoire d'Informatique de Paris-Nord (LIPN),
CNRS(UMR 7030),
99, av. J-B Clément
F-93430, Villetaneuse, France.
Tel: +331 49 40 38 94
Fax: +331 48 26 07 12
https://www-lipn.univ-paris13.fr/~lebbah

Two post-doctoral research associate positions at The Alan Turing Institute: Early Detection of Neurodegenerative Disease

The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence. The Institute is named in honour of the scientist Alan Turing and its mission is to make great leaps in data science and artificial intelligence research in order to change the world for the better.

Position
The Alan Turing Institute has been awarded a grant by Alzheimer’s Research UK (ARUK) to lead the Analytics Hub for the EDoN initiative.

Early Detection of Neurodegenerative diseases (EDoN) is the largest initiative in the world that will collect, share and analyse clinical and digital health data to detect diseases like Alzheimer’s. Ultimately, this approach would be used by doctors to give an earlier and much more accurate diagnosis of dementia diseases.

The Alan Turing Institute is leading on the EDoN Analytics Hub which is tasked with designing and performing the analyses that will allow EDoN to make sense of the data collected in the project. The Analytics Hub is composed of data scientists and is responsible for developing, validating and refining machine learning ‘fingerprint’ models that can detect the diseases that cause dementia at their earliest stage.

The Health and Medical Sciences programme at the Turing delivers research into the theory and methods of AI, statistics, and data analytics underpinning medical and health applications that will enable scientists to do better science, without compromising respect for privacy and patient trust. The Analytics Hub is led by PI, Professor Richard Everson, and is recruiting two Senior Research Associate/Research Associates to support the data analytics and modelling.

Both roles are offed on a fixed term basis for 24 months. The annual salary is £37,000-£42,000 plus excellent benefits, including flexible working and family friendly policies, https://www.turing.ac.uk/work-turing/why-work-turing/employee-benefits

Please see full advertisement, job description and links to application form.



ROLE PURPOSE

The Research Associate will work closely with PI Professor Richard Everson and the Analytics Hub to deliver the data analytics and modelling aspects of the Analytics Hub. The post-holder will explore existing and develop novel machine learning methods to model and analyse retrospective and prospective data collected by the EDoN project and held in the Turing Secure Research Environment.

This role represents an outstanding opportunity to influence the direction of data intensive research to improve millions of people’s lives. You will develop novel methods to, for example, reduce misclassification of individuals due to co-morbidities, accurately predict particular disease subtypes, detect and model cognitive decline, combine multi-modal datasets. Initial work will be on retrospective data, but we will rapidly move to novel forms of data collected on low-burden digital platforms, such as smart phones and wearable technologies. Particular attention will be paid to issues around interpretability and reproducible research. You will produce breakthrough research in machine learning and data science for the early detection of neurodegenerative disease, publishing in top-rated journals and conferences.

DUTIES AND AREAS OF RESPONSIBILITY

Design and implement cutting edge statistical and machine learning methods to detect cognitive decline and dementia-causing diseases. Demonstrate internally across the EDoN consortia and the broader health data science communities, how data science and AI methods can provide predictive modelling to help clinicians in the detection of dementia.
Compare the power of cognitive, neuroimaging and digital markers in retrospective and prospective cohorts to accurately detect dementia.
Determine the integrity of digital markers and estimating the scale of data collection that will be necessary for the EDoN project’s overarching vision of detecting diseases like Alzheimer’s years before the symptoms of dementia start.
Analyse initial prospective data from the Predictors of COgnitive DECline in attenders of memory clinic (CODEC) study based at the Essex Neurocognitive Clinic along with other pilot data to determine the integrity of digital markers and estimating the scale of data collection that will be necessary.


Requirements
Essential

A PhD (or equivalent experience and/or qualifications) in a relevant area, which will include Statistics, Mathematics, Engineering, Computer Science, or related discipline.
Strong background in one or more of the following areas: Bayesian inference, ensemble models, multivariate time-series analysis, medical image analysis.
Experience managing, structuring and analysing research data.
An understanding of the importance of good practices for producing reliable software and reproducible analyses (e.g., version control, issue tracking, automated testing, package management, literate analysis tools such as Jupyter and Rmarkdown.
Excellent written and verbal communication skills, including experience in the visual representation of quantitative data, the ability to write for publication, present research proposals and results, and represent the research group at meetings.
Ability to carry out original research and to produce published research papers.
Please see the Job description for a full breakdown of the duties and responsibilities as well as the person specification. https://cezanneondemand.intervieweb.it/turing/jobs/research_associate__edon_initiative_18886/en/