NVIDIA 2027 Internships: Ph.D. Research Autonomous Vehicles

Potential internships 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 

Enforcing Consistency 

• Foundation and Multimodal Models 

Vision-language models 

Multimodal reasoning 

Spatial Multimodal Models 

Modality Alignment 

Model Scaling 

Synthetic data 

• Inference 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 

Mapless driving 

World Representations 

Beyond imitation learning 

Safety-aware end-to-end models 

• Perception and Representation Learning 

Multi-modal sensor fusion 

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

• Safe and Trustworthy Autonomous Systems 

Principled robustness 

Model explainability 

Control Barrier Functions (CBFs) 

Verification and Validation of Safety-Critical AI Systems 

Trustworthy AI/ML for autonomy and robotics 

• Data-Strategies for AI 

• Benchmarking AV 

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