Follow advances in Physical AI, read papers, reproduce representative algorithms and models on classical computing platforms such as GPUs, and help establish training and evaluation baselines.
Help researchers implement new algorithms and methods that integrate quantum computing. Implement assigned modules, including work to translate problems into quantum-solvable formulations such as Ising or QUBO.
Run and tune algorithms and models on quantum computing platforms. Help design and conduct comparisons of quantum and classical approaches, documenting experiments and analyzing results.
Under guidance, help deploy, debug, and validate models on real robots.
Help implement and improve algorithm modules for data refinement, augmentation, and quality evaluation.
Maintain experiment records and technical documentation so that results are reproducible and can be handed over.
Requirements
Currently pursuing a master’s degree or PhD in computer science, artificial intelligence, automation, robotics, or a related field; exceptional undergraduates will also be considered. At least six consecutive months of internship availability is mandatory; short internships are not offered. Availability on site for at least four days per week is preferred.
Strong programming skills: proficiency in Python, knowledge of C/C++, and familiarity with deep learning frameworks such as PyTorch and the fundamentals of GPU training and inference.
Proficiency with AI agent tools such as Codex or Claude Code, or experience building and using your own agents. You are welcome to demonstrate a project, experiment, or demo created in collaboration with AI during the interview.
Substantial experience in at least one of the following areas (one is sufficient; experience in all areas is not required):
Embodied AI development: experience with simulation platforms such as Isaac Sim / Isaac Lab or the ROS/ROS2 ecosystem, and participation in robotics projects or competitions. Hands-on experience deploying and debugging real robots is preferred.
Model reproduction and training: reproducing Physical AI models from papers, such as Diffusion Policies, VLA/VLM, reinforcement learning, or world models, or substantial deep learning project experience. Experience validating models on real robots is preferred.
Quantum computing: experience using quantum computing platforms or implementing QUBO/Ising models or quantum-inspired algorithms.
Knowledge of mainstream Physical AI models and algorithms, and the ability to quickly understand and reproduce papers.
Basic knowledge of quantum computing, or a strong willingness to quickly learn relevant methods and tools.
Clear communication of your work and conclusions, and effective collaboration with researchers and other colleagues.
Preferred qualifications
Publications in Physical AI, embodied AI, spatial reasoning, Gaussian Splatting, or related fields at conferences such as CoRL, RSS, ICRA, IROS, NeurIPS, ICLR, and CVPR, or journals such as T-RO, IJRR, and RA-L.
Project or deployment experience in bimanual manipulation, dexterous hand manipulation, or whole-body motion control.
Open-source contributions, model deployment performance optimization, or experience delivering real projects.
How to apply
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Please state the internship duration and number of days per week you can commit to.
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