PhD Position in Reinforcement Learning for Automated Driving

il y a 3 semaines


Paris, Île-de-France INRIA Temps plein

Job Context and Requirements

You will work within the ASTRA team, a joint team of scientists from INRIA and Valeo, led by Fawzi Nashashibi and Benazouz Bradai. The team designs models and algorithms for intelligent transport systems, involved in several projects financed by the French National Research Agency.

The PhD thesis focuses on decision-making and planning for automated driving using reinforcement learning. It explores how autonomous vehicles make decisions and plan their actions while considering safety, comfort constraints, and interactions with other road users.

Decision-making systems must generate collision-free trajectories in dynamic environments while anticipating the movements of other road users. The use of reinforcement learning offers promising opportunities to enhance driving policies, trajectory planning, and decision-making processes.

Research Objectives

During the PhD thesis, the general objective is to:

  • Develop an RL-based decision-making and planning framework for automated driving systems.
  • Optimize driving policies using RL algorithms to ensure safety, efficiency, and adaptability.
  • Evaluate the performance of the proposed RL system through simulations and real-world testing.

Main Activities

Main activities include research, writing scientific papers, presenting work at scientific conferences, programming, data management, interacting with partners, writing a doctoral thesis, and participating in demonstrations and showcases.

Candidate Profile

The candidate should have a Master's Degree in the relevant field, a strong background in machine learning, particularly reinforcement learning, proficiency in programming languages such as Python and C++, and a solid understanding of robotic systems and control challenges.

Language Skills

The candidate should have a good level of English for communication and French is optional but desirable.

Additional Skills

The candidate should have strong problem-solving skills, the ability to work independently and collaboratively, and a strong ability to work in groups.



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