Job offer

CNRS
Canton of Toulouse-5, France
4 days ago

Role details

Contract type
Internship / Graduate position
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
French

Job location

Canton of Toulouse-5, France

Tech stack

Artificial Intelligence
Distributed Systems
Python
Matlab
Machine Learning
Reinforcement Learning
Information Technology

Requirements

The recruit will conduct research on the development and analysis of reinforcement learning algorithms for networked and communicating systems. The objective is to combine learning methods and network models in order to optimize sequential decisions (routing, resource allocation, scheduling) in uncertain environments.

  • Develop and analyze reinforcement learning algorithms adapted to networked systems.
  • Study sequential decision problems in contexts such as routing, resource allocation or queuing networks.
  • Contribute to the theoretical modeling and performance analysis of learning algorithms.
  • Implement and experimentally evaluate the proposed approaches.
  • Participate in the writing of scientific articles and the dissemination of results.
  • Collaborate with researchers from the IRIT laboratory as well as with associated scientific partners.

Le poste est basé à l'IRIT (Institut de Recherche en Informatique de Toulouse), un laboratoire majeur en informatique regroupant plusieurs centaines de chercheurs et doctorants. La personne recrutée sera accueilli·e au sein du département ASR (Architecture, Systèmes et Réseaux), dont les thématiques couvrent notamment les réseaux, les systèmes distribués et l'apprentissage automatique appliqué aux systèmes. Le projet s'inscrit dans un environnement scientifique dynamique, avec des collaborations possibles avec plusieurs chercheurs du laboratoire travaillant sur l'apprentissage par renforcement et les systèmes en réseau, ainsi qu'avec l'écosystème toulousain de recherche en intelligence artificielle, notamment dans le cadre de la chaire ANITI dédiée à l'apprentissage par renforcement., Solid training in machine learning, particularly in reinforcement learning.

  • Good knowledge of probability, stochastic processes and optimization
  • Skills in modeling and analysis of algorithms. - Experience in scientific programming (Python, Matlab or equivalent).
  • Interest in networked systems, distributed systems or communication networks.
  • Ability to conduct research independently and collaborate in an international scientific environment.
  • Excellent scientific communication skills (writing articles, presentations).

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