Research Scientist Intern, State Estimation for Dexterous Manipulation (PhD)
Responsibilities
Design and implement latent space representations for object physical state during robotics manipulation tasks that go beyond fixed parameter sets.
Design and execute controlled experiments to validate the representation: measuring adaptation speed, property decoding fidelity, and downstream control performance against baselines (no object state, explicit physical parameters, raw sensor history).
Benchmark the latent state representation on practical dexterous manipulation tasks.
Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
Showcase the value in simulated or physical demos.
Requirements
Currently has or is in the process of obtaining a Ph.D. degree in Robotics, Machine Learning, Computer Science, or relevant technical field.
Strong background in representation learning, generative models, or neural implicit representations (e.g., Gaussian splatting, NeRF, structured latent variable models).
Experience with physics-based estimation, state estimation, or system identification in robotic or dynamical systems (e.g. Bayesian filtering, online adaptation, or meta-learning for system identification).
Experience with Python and PyTorch.
Preferred Qualifications
Experience with tactile sensing, force/torque sensors, or robot hand manipulation.
Familiarity with model-based control (MPC), reinforcement learning, or imitation learning for manipulation.
Experience with Bayesian filtering, online adaptation, or meta-learning for system identification.
Experience with experimental design and statistical evaluation of robotic systems.
Experience working and communicating cross functionally in a team environment.
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as Robotics (RSS, ICRA, IROS, CoRL, T-RO, IJRR), Machine Learning (NeurIPS, ICML, ICLR, AAAI, JMLR), and Computer Vision (CVPR, ICCV, ECCV, TPAMI), or similar.
Intent to return to the degree program after the completion of the internship/co-op.