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PhD Position in Reinforcement Learning for Automated Driving
Il y a 2 mois
Context and Advantages of the Position
We are seeking a highly motivated PhD student to join our team at INRIA, working on the development of decision-making and planning frameworks for automated driving systems using reinforcement learning (RL). The successful candidate will contribute to the design and implementation of RL-based decision-making and planning algorithms for autonomous vehicles, with a focus on safety, efficiency, and adaptability.
Research Objectives
The PhD thesis will focus on the following research objectives:
- 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
The successful candidate will be responsible for:
- Research and development of RL-based decision-making and planning algorithms.
- Implementation and testing of the proposed algorithms in simulation and real-world environments.
- Collaboration with the research team to ensure the successful completion of the PhD thesis.
Requirements
To be eligible for this position, the candidate should have:
- A Master's degree in a relevant field (e.g., computer science, robotics, artificial intelligence).
- Strong background in machine learning, particularly reinforcement learning.
- Proficiency in programming languages such as Python, C++.
- Solid understanding of robotic systems and control challenges.
- Familiarity with automated vehicles simulation environments.
- Experience in developing control algorithms or motion planning strategies for autonomous vehicles.
Language
The working language of the team is English, and proficiency in French is not required but highly desirable.
Benefits
The successful candidate will benefit from:
- Subsidized meals.
- Partial reimbursement of public transport costs.
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave.
- Possibility of teleworking and flexible organization of working hours.
- Professional equipment available.
- Access to vocational training.
- Social security coverage.
