Description
NVIDIA is seeking a PhD research intern to advance world foundation models within NVIDIA Cosmos for Physical AI.
You will work with NVIDIA researchers and engineers to define and pursue a focused research project, develop new methods, and demonstrate measurable improvements through rigorous experimentation.
Responsibilities
- Research and develop model architectures, training objectives, and learning algorithms for video generation and world modeling, including diffusion or flow-matching approaches, multimodal conditioning, and spatial and temporal representations.
- Design controlled experiments and ablation studies, combining quantitative benchmarks with qualitative evaluation to diagnose model behavior, identify failure modes, and validate improvements.
- Explore training and inference efficiency through architectural changes, distillation, model compression, or other optimization techniques.
- Develop data curation and evaluation workflows, including model-assisted filtering, annotation, and quality assessment, and measure their impact on model performance.
- Throughout your project, you will collaborate with research and engineering teams and share findings through reproducible implementations, technical reports, and presentations.
Requirements
- Currently pursuing a PhD in Computer Science, Computer Graphics, Computer Engineering, Robotics, or a closely related field.
- Research experience in one or more relevant areas, such as generative modeling, computer vision, video understanding or generation, multi-modal learning, 3D vision, or robotics.
- Strong proficiency in Python and PyTorch, demonstrated through research implementations or substantial projects.
- Hands-on experience implementing, training or fine-tuning, and evaluating deep learning models.
- Ability to formulate research questions, design rigorous experiments, analyze results, and communicate findings clearly.
- Comfortable working in Linux and using standard development practices for ML research, including version control, testing, and experiment reproducibility.
Nice to Have
- Publications at venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related conferences, or substantial research projects demonstrating original technical contributions.
- Background with human appearance or motion modeling, human-object interactions, 3D generative models, 3D/4D representations, or neural rendering.
- Experience assessing physical plausibility, diagnosing visual and temporal artifacts, or developing evaluations using perceptual metrics, human feedback, or learned evaluators.
- Experience with distributed training, GPU performance profiling, efficient data loading, or model acceleration.
- Prior experience with NVIDIA Cosmos is welcome but not required.