Learning Enhanced Motion Planning - Honda Research Institute USA

Learning Enhanced Motion Planning

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Learning Enhanced Motion Planning

Job Number: P23INT-41
During the time of employment, you are expected to design and implement deep learning models tailored to enhance motion planning and decision-making processes in automated mobility systems. This will include implementing state-of-the-art deep learning models, prediction models, and motion planning algorithms. This is an opportunity for applied work in optimizations, deep learning, and controls.
San Jose, CA

 

Key Responsibilities

  • Conduct literature reviews and stay up-to-date with the latest advancements in deep learning, motion planning, and decision-making algorithms.
  • Design and implement deep learning models tailored to enhance motion planning and decision-making processes in automated mobility systems.
  • Develop software prototypes and algorithms for real-time motion planning and decision making using deep learning frameworks such as TensorFlow or PyTorch.
  • Submit a paper to a top-tier control, robotics, or vehicular research conference or journals.

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Minimum Qualifications

  • M.S. candidate in robotics, computer science, electrical engineering, or related field
  • Research experience in planning, machine learning, or robotics
  • Excellent programming skills in C++ and Python

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Bonus Qualifications

  • Ph.D. candidate in robotics, computer science, electrical engineering, or related field
  • Experience with ROS (Robot Operating System)
  • Publication record of planning or prediction on robotic systems

 

Desired Start Date 09/16/2024 

 

Duration

 

3 Months

 

Alternate Way to Apply

Send an e-mail to careers@honda-ri.com with the following:
- Subject line including the job number(s) you are applying for 
- Recent CV 
- A cover letter highlighting relevant background (Optional)

Please, do not contact our office to inquiry about your application status.