Nouvelles
Veuillez trouver ci-joint une offre de postdoc dans l'équipe SequeL à Lille, en apprentissage par renforcement avec applications à l'éducation.
Date limite pour postuler : 28 février 2020.
Merci de la relayer à des personnes susceptibles d'être intéressées.
Les candidats peuvent postuler directement sur le site : https://jobs.inria.fr/public/classic/en/offres/2020-02359
2020-02359 - Post-Doctoral Research Visit F/M Learning controllable representations that evolve over time
Contract type : Fixed-term contract
Level of qualifications required : PhD or equivalent
Fonction : Post-Doctoral Research Visit
# About the research centre or Inria department
The Inria Lille - Nord Europe Research Centre was founded in 2008 and employs a staff of 360, including 300 scientists working in sixteen research teams. Recognised for its outstanding contribution to the socio-economic development of the Hauts-De-France région, the Inria Lille - Nord Europe Research Centre undertakes research in the field of computer science in collaboration with a range of academic, institutional and industrial partners.
The strategy of the Centre is to develop an internationally renowned centre of excellence with a significant impact on the City of Lille and its surrounding area. It works to achieve this by pursuing a range of ambitious research projects in such fields of computer science as the intelligence of data and adaptive software systems. Building on the synergies between research and industry, Inria is a major contributor to skills and technology transfer in the field of computer science.
# Context
The Inria team SequeL is a very active, united, hard-working, internationally renowned and connected research team specialized on theoretical and applied aspects of machine learning for sequential decision making with noisy or partial feedback. It is focused on reinforcement, bandit learning, especially in non-stationary environments.
Our work spans from learning theory, to the design of efficient algorithms, to applications. Our team led to many publications in top conferences such as NeurIPS, ICML, ALT, COLT, AISTATS.
# Assignment
In order to act, an agent should learn a representation of the world. Hopefully, the representation of states should be controllable: by learning a representation of the actions, the agent can act on the representations of states. In order for these representations to be meaningful, the actions should modify few independently controllable features of the representations.
For example, in educational assessments, we can learn the latent ability of students as we ask them questions. These latent abilities evolve over time, and lessons act on these representations.
As main outcome, we expect publications at top conferences and journals in machine learning or data mining.
Supervision: Jill-Jênn Vie
# Skills
deep generative models
reinforcement learning
recommender systems
Experience in various areas is a plus :
privacy-preserving ML
fairness
causal inference
applications to education or healthcare.
# Benefits package
Partial reimbursement of public transport costs
Subsidized meals
Leave : 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours)
Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
Professional equipment available (videoconferencing, loan of computer equipment, etc.)
Access to vocational training
Possibility of French courses
Social, cultural and sports events and activities
Administrative support : Social security coverage/ Help for Housing / Scientific Resident card and help for visa
# Remuneration
Gross monthly salary (before taxes) : 2653 €