NVIDIA is searching for a world-class intern to join our growing research team. The ideal candidate will be conducting cutting-edge research at the intersection of Machine Learning, Computer Vision and Computer Graphics, and working alongside top experts in these fields. With incredible resources in AI, graphics and robotics, you will be able to impact, contribute and advance these exciting domains. Topics include but are not limited to AI for simulation, 3D Deep Learning, DL for animation, content generation, transfer learning, domain adaptation, computer vision, and medical imaging. With its unique open culture, NVIDIA is one of the best industry labs to do AI research.

Image recognition and speech recognition - GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA's GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as "the AI computing company."

• Invent or apply deep learning techniques to the field of 3D or 4D content creation (photogrammetry, generative AI).
• Work on improving realism and immersive qualities of VR environments and interactions, including creating content with AI techniques, intelligent characters, and behavior of crowds and traffic.
• Participate in projects to conduct Deep Learning research and publish papers to well known conferences (ex. CVPR, NeurIPS)
• Product-oriented research for deep learning technology in Games, Virtual Reality, and other applications.

• Currently pursuing a Ph.D. in Computer Science/Engineering, Electrical Engineering, Math/Physics/Statistics or a related field
• Minimum 1 course on theories and practices of machine learning, including common machine learning frameworks
• Domain expertise to drive new research at the intersection of machine learning and visual computing, in domains including video modeling, 3D re=construction, computer vision, generative AI, robotics, creative tools, geometry processing, human motion, and more

• In-depth knowledge of the theory and practice of machine learning
• Minimum 1 strong publication record (SIGGRAPH, NeurIPS, ICML, AAAI, CVPR, ECCV, ICCV, etc).
• Knowledge of common machine learning frameworks such as PyTorch, JAX, Warp, CUDA