Scholarships and Funding Opportunities

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PhD position at LIRIS/INSA-Lyon on Deep Learning for Robotics

We have a funded PhD position at LIRIS and INSA-Lyon on “Deep Learning and Robotics”
Supervisors
Begin: September 2021
Duration: 36 months
Funded by the Remember project (AI chair):
More information on the position:
We are a strong group and target excellent research with publications in top-level conferences and journals.
Feel free to apply if you have excellent academic records.
Topic
 
This thesis will deal with methodological contributions (models and algorithms) for the training of real and virtual agents allowing them to learn to solve complex tasks independently. Indeed, intelligent agents require high-level reasoning skills, awareness of their environment and the ability to make the right decisions at the right time [1]. The decision-making policies required are complex because they involve large observation and state spaces, partially observed problems, and largely nonlinear and intricate interdependencies. We believe that their learning will depend on the ability of the algorithm to learn compact representations of memory structured spatially and semantically, capable of capturing complex regularities of the environment and of the task in question.
A key requirement is the ability to learn these representations with minimal human intervention and annotation, as manual design of complex representations is almost impossible. It requires the efficient use of raw data and the discovery of patterns through different learning formalisms: supervised, unsupervised or self-supervised, by reward or by intrinsic motivation [6,7], etc.
Another key issue is correct network structure (inductive bias). Past work of the group explored spatial maps with topological [1] or metric [2] structure (Figure 1), and current work looks into transformers, which we have recently successfully applied to video processing [4].
Explainability of the developed models will also be an issue, and explored [5,8]
References of the group
 
[1] Edward Beeching, Jilles Dibangoye, Olivier Simonin and Christian Wolf. Learning to plan with uncertain topological maps. To appear in European Conference on Computer Vision (ECCV), 2020
[2] Edward Beeching, Jilles Dibangoye, Olivier Simonin and Christian Wolf. EgoMap: Projective mapping and structured egocentric memory for Deep RL. To appear in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020.
[3] Edward Beeching, Christian Wolf, Jilles Dibangoye and Olivier Simonin. Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer. To appear in International Conference on Pattern Recognition (ICPR), 2020.
[4] Brendan Duke, Abdalla Ahmed, Christian Wolf, Parham Aarabi and Graham W. Taylor. SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation To appear in International Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (oral presentation).
[5] Théo Jaunet, Romain Vuillemot and Christian Wolf. DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning. In Computer Graphics Forum (Proceedings of Eurovis), 2020.
[6] A. Aubret, L. Matignon and S. Hassas, A survey on intrinsic motivation in reinforcement learning, arXiv preprint arXiv:1908.06976
[7] A. Aubret, L. Matignon and S. Hassas. ELSIM: end-to-end learning of reusable skills through intrinsic motivation. To appear in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020.
[8] Corentin Kervadec, Grigory Antipov, Moez Baccouche and Christian Wolf. Roses Are Red, Violets Are Blue… but Should VQA Expect Them To? To appear in International Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

PhD and Postdoc Positions in Bordeaux, France

Topic: Intelligent Handling of Imperfect Information
 
The LaBRI research lab based at the University of Bordeaux is currently seeking 
highly motivated candidates with experience in knowledge representation and reasoning
(in particular, description logics), databases, or logic in computer science to take part in the 
INTENDED AI Chair  (https://intended.labri.fr/), whose aim is to develop principled 
and effective methods for handling imperfect data.
 
Currently, one PhD position and one 2-year postdoc position are available. Details on
the positions, research environment, and requirements can be found here: 
 
 
The starting date is flexible (ideally, Fall 2021). The positions will remain open until filled, 
but to receive full consideration, applications should be submitted by May 16, 2021.
 
If you’re interested in applying, or want more information, please get in touch!
 
Best regards,
 
Meghyn Bienvenu
CNRS Researcher at LaBRI, Bordeaux

Seed Grant for New African Principal Investigators

We are pleased to announce a new TWAS Research Grant Programme, through the support of the German Federal Ministry of Education and Research (BMBF), Seed Grant for New African Principal Investigators (SG-NAPI). The call for 2021 is now open and will remain open until 27 July 2021.

The programme is specifically aimed at PhD graduates from selected African countries, who obtained their PhD abroad within the last 5 years and returned home within the last 36 months or will return home before the end of 2021.

Attached is a flyer for the programme that can be circulated electronically or printed and put up at university notice boards. We appreciate your help with the dissemination of this new programme.

Full details about the SG-NAPI Programme are available on the TWAS website

For any queries, kindly write to sgnapi@twas.org

Thanking you.

Kind regards,

TWAS Science Diplomacy Programme


Ph.D. – Explainable Artificial Intelligence and therapeutic target identification

Oncodesign SA and the Distributed Artificial Intelligence and Knowledge laboratory (CIAD LAB – UMR 7533) at the University of Bourgogne Franche-Comte have a vacancy for a Ph.D. fellowship.

 

About the position 

————–

Biological networks are very effective tools for modeling, analyzing, and discovering new biological interactions in complex biological systems. 

In recent years, network models and algorithms have been used for the development of precision medicine for many diseases. 

The mathematical machinery at the heart of this research field is based on graph theory, a widely studied disciplinary field. 

This is also associated with machine learning on structured data in the form of graphs. 

A big challenge is to create better modeling tools to integrate human expertise and artificial intelligence techniques to exploit big data for clinical research and drug development,  to advance a better understanding of health and disease and formulate a hypothesis on a new mechanism of actions and identify corresponding innovative therapeutic targets. . To meet this challenge, many emerging works propose the design of explainable AIs, allowing the identification of innovative therapeutic targets. These explainable AIs combine connectionist AI approaches such as deep learning, neural networks, etc., and causal AIs based on modeling causal graphs of knowledge derived from the knowledge of domain experts. 

 

This research will address questions such as:

 

–  How to aggregate data sources from heterogeneous biological and medical databases while maintaining the consistency of the associated knowledge?

–  What are the best functions for analyzing raw data to extract knowledge?

–  How to develop in silico prediction of new therapeutic targets which will then be validated in vitro and/or in vivo? 

 

Required selection criteria

————–

The qualification requirement is that you have completed a master’s degree with a strong academic background in one or more of biology, computer science, and engineering, mathematics, or equivalent education with a grade of the first third of the promotion. 

The candidate must have a background in computer science with ideally skills in machine learning and/or knowledge engineering. 

Knowledge in the field of biology will be required.

Applicants must provide evidence of good English language skills, written and spoken. Mastery of the French language will be appreciated.

 

Preferred selection criteria

–  Background in Artificial Intelligence and/or Data Mining/Data Science applied in medicine and  biology

–  A candidate with some industrial experience in the aforementioned areas will get preference.

–  Publication activities in the aforementioned disciplines will be considered an advantage.

 

Salary and conditions

————–

Ph.D. candidates are remunerated by the company. The amount of the salary can be negotiated with the company. 

The appointment is for a term of 3 years and can be extended beyond the Ph.D. defense.

Appointment to a Ph.D. position requires that you are admitted to the Ph.D. program in computer science and that you participate in an organized Ph.D. program during the employment period.

 

About the application

————–

This research is funded by the French government and the Oncodesign SA (https://www.oncodesign.com/en/) in the frame of CIFRE Ph.D. (Conventions Industrielle de Formation par la Recherche). 

Oncodesign and the CIAD laboratory have initiated a scientific collaboration in the field of precision medicine. 

This collaboration concerns the identification of new therapeutic targets and the acceleration of the research and development phases of new molecules. 

The job will be located at Dijon, a gastronomic and touristic French city at 1.5 hours from Paris by train. 

The CIAD Lab and Oncodesign are 500 meters distance.

 

Application deadline: 30.06.2021

————–

If you have any questions about the position, please contact: Christophe Nicolle email: cnicolle@u-bourgogne.fr

Dr. Ouassila LABBANI NARSIS
Maître de Conférences – CNU 27
Laboratoire Connaissance et Intelligence Artificielle Distribuées – UMR 7533
Université de Bourgogne Franche-Comté
Institut Marey Maison de la Métallurgie (I3M) – 64 rue de Sully – 21000 Dijon

PhD position between Nantes and Montréal in transport optimization

We are looking for a new PhD student on a subject related to transport optimization under uncertainty in smart cities.

The thesis will be co-hosted between HEC Montréal (CIRRELT and IVADO labs) and IMT Atlantique in Nantes (LS2N).

Please find the subject description in the attached document.

Best regards,
Ph.D. student-announcement-20210413 Thesis_Montreal_Nantes_Transport_Optimization
Maria Restrepo, Jorge Mendoza, Fabien Lehuédé


PhD positions in online computation at Sorbonne University

Two PhD positions are available (subject to funding approval) at the Laboratoire d’informatique de Paris 6 (LIP6) at Sorbonne University. The topics are at the intersection of Theoretical Computer Science, AI and Operations Research. The proposals are on the topic of online computation and can be accessed at the following links:

https://www.lip6.fr/recherche/emploi-fiche.php?ident=E64

and

https://www.lip6.fr/recherche/emploi-fiche.php?ident=E65

Candidate profile:

— B.Sc. and M.Sc. in Computer Science or related discipline such as Applied Mathematics.

— Strong background and interest in theoretical analysis of algorithms. Programming experience and willingness to work with real data will be considered a plus.

— Good command of English.

Applications received by April 30 will receive full consideration. Late applications will be considered as long as the positions are not filled, but not beyond May 15. Applicants should submit the following by email:

— Copies of degrees (if in progress, provide expected date of graduation), and full transcripts of grades.

— Detailed CV.

— Cover letter (explaining the motivation behind the application).

— 1-2 reference letters, sent directly to the supervisor.

Spyros Angelopoulos
CNRS and LIP6-Sorbonne University


Ph. D position announcement

We will be thankful if you can help us to disseminate this Ph. D position announcement.
And sorry for multiple receptions if any.

Best regards,
Ph.D. student-announcement-20210413


Prof. Amal El Fallah Seghrouchni
Sorbonne Université – LIP6
Head of MAS group @ lip6
Co-Head of A.I. and Data Science Track @ LIP6
http://www-poleia.lip6.fr/~elfallah/
https://twitter.com/amalelfallah


3 post-doc / research associate positions in neural reinforcement learning

Three post-doc / research associate openings to work closely together as part of a ~3 year Chist-Era project, to enhance neural reinforcement learning with causal inference methods for explainable actions (https://www.chistera.eu/projects/causalxrl), starting in ~Q2-Q3, 2021:
-> At the University of Sheffield, UK with Dr Aditya Gilra and Prof Eleni Vasilaki — https://www.jobs.ac.uk/job/CEH708/research-associate-in-causal-explainable-reinforcement-learning-causalxrl (Deadline: 14th April 2021).
-> At the University of Vienna, Austria with Prof Moritz Grosse-Wentrup — https://ni.cs.univie.ac.at/ (ad to appear).
-> At INRIA Lille, France with Prof Philippe Preux — https://jobs.inria.fr/public/classic/en/offres/2020-03209 (Deadline: 31st May 2021).
Please feel free to email back for any further information.
Best,
Aditya.

Postdoc position in Marseille

We are looking for candidates for an interdisciplinary postdoc : Computer science and application to biology.

Host team: LIRICA from the LIS laboratory, Aix-Marseille university

Duration: 2 years, beginning December 2021

Subject: The objective of the postdoc is to study the use of the Answer Set Programming (ASP) paradigm for the representation and the computation of attractors in gene regulation networks and its application in the biological framework of gene networks of a timorous liver of a mouse.

Link to the detailed offer: https://centuri-livingsyshtems.org/pdp2021-13/

More information and application process: https://centuri-livingsystems.org/recruitment/

Application deadline : April 16, 2021

Belaid Benhamou


Postdoc positions in Naples

I post this announcement for two excellent postdoc positions in Naples (Italy) supported by the EU H2020 Project TRAPEZE:
TRAnsparency, Privacy and security for European citiZEns

The postdoc positions are supervised by :

Piero Bonatti: pieroandrea.bonatti@unina.it
LuigiSauro: luigi.sauro@unina.it

Project and position description are in TRAPEZE-scholarship-call-V2.

Best wishes
Nicola Olivetti


Research fellow in AI for autonomous driving

The School of Engineering, Computing and Mathematics at Oxford Brookes University is seeking a Research Fellow in Artificial Intelligence for Autonomous Driving, to be appointed as soon as possible, for a duration of 16 months. The Fellow will be appointed at Grade 8, with a starting salary of £31,866 per annum, rising annually to £34,804.

The deadline for application is April 27 2021.

The successful candidate will lead the School’s efforts in the area of human-aware AI for autonomous driving, and will be assisted by one or more PhD students.

The project concerns the design and development of novel ways for autonomous vehicles to interact with humans, with a focus on autonomous racing cars. Challenging, disruptive applications of AI require forms of communication between humans and machines which go much beyond the current level of sophistication, towards the modelling of agent thinking in a machine theory of mind approach (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7253617/) and robust formulations of inverse reinforcement learning under uncertainty.

The Fellow will work to design and implement a prototype (but complete) pipeline in a simulated scenario, including: (i) the design of simulations allowing smart cars to understand the reasoning and intentions of other drivers and pedestrians; (ii) the making of decisions based on the results of these simulations; (iii) the actual control and path planning required to pursue the best course of action, with demonstration in a simulated environment.

As part of this project, a new ROad event Awareness Dataset for Autonomous Driving (ROAD) (https://arxiv.org/abs/2102.11585), the first in the world of its kind, is being released as part of the upcoming ICCV 2021 Workshop: “The ROAD challenge: Event detection for Situation awareness in autonomous driving”:

https://sites.google.com/view/roadchallangeiccv2021/home?authuser=0

The Fellow will be tasked with supervising MSc and final year students working on the subject, and will work jointly with the Visual Artificial Intelligence Laboratory, led by Prof Fabio Cuzzolin, and the Autonomous Driving research group, led by Dr Andrew Bradley.

The Visual Artificial Intelligence Laboratory (http://cms.brookes.ac.uk/staff/FabioCuzzolin/) is a thriving research unit projected to comprise 30+ members in 2021, one of the top research groups in the world in deep learning for action detection, currently pioneering frontier topics such as theory of mind, continual learning, self-supervised learning and epistemic artificial intelligence for AI safety.

The Engineering section has a very strong reputation in motorsports and engagement with F1 teams. Oxford Brookes Racing having been crowned Class 1 Runner Up in the 2018 Formula Student competition. After the 3rd place achieved in 2019, Oxford Brookes Racing – Autonomous (which includes ca 60 UG students), led by Bradley and assisted by Cuzzolin, ranked 1st overall in the 2020 IMechE Formula Student – AI competition for autonomous racing cars:

https://www.imeche.org/events/formula-student/team-information/fs-ai

The Fellow will be expected to co/lead OBR – Autonomous towards achieving its next objectives.

You are encouraged to contact Prof Cuzzolin at fabio.cuzzolin@brookes.ac.uk for more information and informal feedback on your application.

To formally apply, please follow the instructions provided here:

https://www.jobs.ac.uk/job/CFF375/research-fellow-in-ai-for-autonomous-driving


PhD in Algorithms for Star Discrepancy Problems

We are looking for a PhD candidate to do research on efficient
algorithms for star discrepancy problems at LIP6, Sorbonne Université,
Paris, France. Extended research visits to the University of Coimbra,
Portugal, are also possible.

Star discrepancy measures how regularly a set of points is distributed
in a given space. Point sets of low star discrepancy have several
important applications including Quasi-Monte Carlo integration,
financial mathematics, optimization, design of experiments, and many
more. The main goal of this PhD project is the design and the analysis
of efficient algorithms to address the discrepancy subset selection
problem, that is, to find a subset of a point set that minimizes star
discrepancy.

The PhD student will be supervised by Carola Doerr from LIP6, Sorbonne
Université and Luís Paquete from the University of Coimbra.  Applicants
are expected to have excellent skills on design and analysis of
algorithms.

The earliest starting date is October 2021.
Firm application deadline is May 16.
Candidates are strongly advised to get in touch with us before submitting their application.

More details about the PhD project are available at

http://www-ia.lip6.fr/~doerr/2021-PhD-Discrepancy.pdf

Questions about the position should be sent to Carola.Doerr at lip6.fr
or/and paquete at dei.uc.pt.

With best wishes,
Carola Doerr, CNRS, LIP6, Sorbonne Université


Carola Doerr, CNRS researcher at LIP6, Sorbonne University, http://www-ia.lip6.fr/~doerr/


PhD position on multi-agent reinforcement learning

A fully funded PhD position is available starting Spring 2022 on multi-agent reinforcement learning, human-robot teaming, and multi-objective planning. Research will be conducted in the Unmanned Systems Lab, Department of Electrical and Computer Engineering at The University of Texas at San Antonio (UTSA), under the supervision of Dr. Yongcan Cao.

Position description:

Required

–       A Bachelor’s degree in electrical engineering, computer science, computer engineering, or a related field;

–       Strong background in mathematics, statistics, and machine learning;

–       Excellent writing and communication skills;

–       Proficiency in Matlab, C++, or Python.

Preferred

–       Master’s degree

–       Experience on Robot Operating System (ROS), reinforcement learning, and computer vision

–       Hands-on experience on robotics (hardware or software)

–       Demonstrated research experience (i.e., projects or publications)

How to apply:

Send the following documents in a single PDF file

–       One page cover letter describing your interest, goal, and how your background fits well;

–       CV or resume

–       Transcripts

to yongcan.cao@utsa.edu.


Internship Opportunity at Amazon Alexa

We are looking for an intern with RL/multi-agent system experience for an internship at Amazon Alexa.

We are working on a problem where we are trying to formulate a typical classification problem as a multi-agent game. This project started from a huge need within Alexa, so it would not only provide the opportunity to publish the work, but also to work on a project that would impact millions of people.

If you are interested, send me an email to fgmz@amazon.com with your resume. I can then provide more information.

Cheers!

Francisco Garcia
Amazon Alexa


PhD position in Strasbourg (Sept. 2021)

A PhD position is open at University of Strasbourg (ICube lab) – France for October 2021

Title:    Multiparadigm interactive collaborative learning of image time series in interaction with the medical expert

Topic: Analysing heterogeneous image time series using supervised methods requires that the classes sought are perfectly known and defined and that the expert is able to provide a sufficient learning data set both in number and quality. Unfortunately, unsupervised methods can lead to results which do not match the potential objects/classes of interest. Faced with these difficulties, we propose to develop an innovative a collaborative framework of interactive multi-paradigm collaborative learning in which different methods, supervised or not, work together to produce “better” result. The objective of the thesis is two-fold. First, it will be to answer scientific questions regarding collaborative process such as what information to share (data, hypotheses, constraints…), how to evaluate results, what are good coordination mechanism, how to combine the opinions of the different agents and how to ensure convergence. Second, it is to enable the expert to add “on the fly” information (labels, constraints, etc.) used to guide the learning process in order to produce clusters and models closer to the expert’s “intuitions”. To do this, the expert will be actively assisted by the system, which will for example offer advice or proposals for new constraints or labelling of objects
Different fields in health domain of application are envisaged to validate our proposition. They will mainly concern situations in which the types of evolution are both numerous and not very formalized as for instance, images time series.

To apply:

The positions are offered to both foreign and French students who hold a Master degree in computer science. The candidate must have good skills in data analysis and more particularly in supervised or unsupervised classification of time series. Skills in image analysis is welcome.

Good knowledge of English (French is not mandatory)

As required by the Doctoral School of the University of Strasbourg, the candidate must have obtained all his/her Master’s (and Bachelor’s) semesters or equivalent with a grade above 12/20. He/she must also be ranked among the top 20% of graduates of his/her Master promotion.

Send your CV, transcript of grades, ranking and motivation to Pierre Gançarski (gancarski@unistra.fr) and Antoine Cornuéjols (antoine.cornuejols@agroparistech.fr)

Detailed Description:  https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/



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