NVIDIA is at the forefront of the AI and robotics revolution, and NVIDIA’s robotics teams are on a mission to build the essential technology that can enable any company to become a robotics company. The Seattle Robotics Lab is focused on fundamental and applied robotics research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, and simulation. This research aims to transform research paradigms, transfer into NVIDIA’s robotics and simulation products, and create new robotics markets for the world. The Seattle Robotics Lab has led many influential works that have been presented at top robotics, AI, and computer vision conferences; these works include BayesSim, cuRobo, DeXtreme, DiSECT, Factory, GraspNet, ITPS, MimicGen, RVT, and SHAC. (See our publications page for a complete list.) The work has deeply impacted the research community and NVIDIA products, including Isaac Sim, Isaac Lab, and Isaac Manipulator. Furthermore, SRL collaborates closely with other research and engineering teams to advance and leverage the Cosmos and GR00T-N foundation models, as well as the Newton physics simulation engine.
We are looking for a PhD research intern to play a pivotal role in our research efforts, providing deep, hands-on technical contributions. These interns must have exceptional research and engineering skills, a strong research track record, and a team-first mindset. You will have the opportunity to achieve real impact, while working with some of the most creative, brilliant, and motivated researchers in the world. Our former interns have gone on to become full-time researchers and engineers at NVIDIA and other world-leading tech companies, started academic careers at top universities, and launched robotics and AI companies.
Note: This internship role will be based at NVIDIA's Zurich office.
What you will be doing:
- Developing algorithms, models, and methods for robotic manipulation and/or loco-manipulation, for both industrial and household applications;
- Integrating these methods into real-world robotic manipulation systems, including those consisting of collaborative robot arms, industrial robot arms, mobile manipulators, humanoids, and dexterous hands;
- Contributing to multi-person research projects that require a diverse set of skills across the robotics and machine learning stack;
- Engaging with the academic community through high-impact publications, conferences, workshops, and/or code releases.
What we need to see:
- Enrolled in a PhD in Robotics, Machine Learning, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field.
- Knowledge of both the theory and practice of robotics and AI, with a strong interest in connecting your work to real-world robotics applications.
- A demonstrated research track record, with work published in top robotics and AI conferences and journals such as RSS, CoRL, ICRA, IROS, IJRR, T-RO, NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, and EMNLP.
- Exceptional communication, collaboration, and interpersonal skills, with significant experience working on a team.
- Exceptional programming skills in Python; in addition, familiarity with C++, CUDA, and Warp is a plus.
- Fluency in modern deep learning frameworks such as PyTorch and JAX.
- Experience with robotics frameworks such as ROS2 and physics simulation frameworks such as Isaac Sim, Isaac Lab, MuJoCo, and/or Newton.
- Comfort in working through the complexities of simulation and real-world robotics, including debugging physics simulators and renderers under rapid development; selecting, setting up, maintaining, and enhancing complex robotics hardware; debugging communication systems; and designing robust workflows for model training and evaluation.
The following research areas and applications are of particular interest:
- Bimanual and dexterous manipulation
- Mobile manipulation and humanoid loco-manipulation
- Multisensory perception (e.g., vision, tactile, and force/torque sensing)
- Simulation, sim-to-real, and real-to-sim
- Vision-language-action (VLA) models, including architectural advancements, large-scale training, and test-time reasoning
- Industrial applications, such as bin-picking, kitting, and assembly