PhD Researcher Intern, World Foundation Models - Korea, Seoul, South Korea
Non-Engineering
Originally posted 29 October 2026 by the employer.
Research world foundation models within NVIDIA Cosmos for Physical AI.
About the role
NVIDIA is seeking a PhD research intern to advance world foundation models within NVIDIA Cosmos for Physical AI. The research focuses on model architectures, training objectives, and efficient training and inference for multimodal generation and action-conditioned prediction.
What you'll do
- 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.
- Collaborate with research and engineering teams to share findings through reproducible implementations, technical reports, and presentations.
What you'll need
- Currently pursuing a PhD in Computer Science, Computer Graphics, Computer Engineering, Robotics, or a closely related field.
- Research experience in generative modeling, computer vision, video understanding or generation, multi-modal learning, 3D vision, or robotics.
- 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.
- Experience working in Linux and using standard development practices for ML research, including version control, testing, and experiment reproducibility.
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