Phd Research Intern
Autonomous Vehicles, 2024
Confirmed live in the last 24 hours
NVIDIA

10,001+ employees

Designer & manufacturer of computer chips & graphics processors
Company Overview
NVIDIA is on a mission to solve the world's most stimulating technology problems – in industries ranging from gaming to scientific exploration.
AI & Machine Learning

Company Stage

N/A

Total Funding

$4.2B

Founded

1993

Headquarters

Santa Clara, California

Growth & Insights
Headcount

6 month growth

5%

1 year growth

0%

2 year growth

15%
Locations
Remote in USA • Santa Clara, CA, USA
Experience Level
Intern
Desired Skills
Python
Communications
Tensorflow
CUDA
Pytorch
CategoriesNew
Lab & Research
AI & Machine Learning
Requirements
  • Pursuing a PhD in Robotics, Computer Science, Computer Engineering or related field
  • Relevant research experience in the field of vehicle / robot autonomy
  • Strong knowledge of theory and practice of vehicle / robot autonomy, or a related area with a strong interest in connecting your work to autonomous vehicles
  • A track record of research excellence with your work published in top conferences and journals such as RSS, ICRA, IJRR, NeurIPS, ICML, CVPR, TAC, etc, and other research artifacts such as software projects
  • Exceptional programming skills in Python; C++ and parallel programming (e.g., CUDA) are a plus
  • Knowledge of common machine learning frameworks such as PyTorch and Tensorflow
  • Strong communication and interpersonal skills are required along with the ability to work in a dynamic, research-focused team
Responsibilities
  • Designing and implementing cutting-edge techniques in the field of vehicle autonomy
  • Publishing your original research
  • Collaborating with other research team members, a diverse set of internal product teams, and external researchers
  • Transferring technology you've developed to relevant product groups
Desired Qualifications
  • Experience with perception systems, machine learning, and robotics
  • Experience with decision making under uncertainty, deep learning, reinforcement learning, and the verification and validation of safety-critical AI systems