Nvidia NVIDIA Internships PhD Autonomous Vehicles Research 

United States, California 

690109470

time type

Full time

posted on

Posted 6 Days Ago

job requisition id

society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create.

with one of our industry leadingAutonomous Vehicles

Learn more about

What you will be doing:

• Design and implementcutting-edge techniques in the field of vehicle autonomy.

• Collaborate with other team members, teams, and/or external researchers.

• Transfer your research to product groups to enable new products or types of products. Deliverable results include prototypes, patents, products, and/or publishing original research.

What we need to see:

• Must be actively enrolled in a university pursuing a PhD degree in Computer Science, Electrical Engineering, or a related field, for the entire duration of the internship.

• Depending on the internship, prior experience or knowledge requirements couldinclude the following programming skills and technologies:

• Python, C++, CUDA, Deep Learning Frameworks (PyTorch,TensorFlow, etc.)

• Strong background in research with publications at top conferences.

• Excellent communication and collaboration skills.

• Experience with large-scale model training is a plus.

nternships require research experience in at least one of the following areas:

Next-Generation AV Architectures

• Chain-of-Thought Reasoning

• Mixture-of-Experts

• Diffusion-LLMs

• Diffusion-based Trajectory Decoding

Novel Policy Training Strategies

• Closed-loop Training

• Off-policy RL

• Online RL

Foundation and Multimodal Models

• Vision-language models

• Multimodal reasoning

• Spatial Multimodal Models

• Modality Alignment

• Model Scaling

• Synthetic data

Efficiency

• Inference Optimizations (e.g., parallel decoding, speculative decoding)

• Token Representations

• Model Distillation

Simulation and Behavior Modeling

• Digital Twins

• Scenario Generation

• World Models

• Behavior/Traffic Modeling

End-to-End AV Systems

• driving

• World Representations

• Beyond imitation learning

• Safety-aware end-to-end models

and Representation Learning

• Multi-modal sensor fusion

• 2D/3D detection, segmentation, depth estimation, scene understanding,neural representations

Autonomous Systems

• Principled robustness

• Model explainability

• Control Barrier Functions (CBFs)

• Verification and Validation of Safety-Critical AI Systems

• AI/ML for autonomy and robotics

Data-Strategies for AI

Benchmarking AV

and other helpful student resources related to our latest technologies and endeavors.