Robotics Applied Scientist, Active Perception

Formulate the active perception problem for inspection and a broad range of robot behaviors

Develop viewpoint planning policies for active information gathering

Research calibrated confidence and abstention for vision-language reasoning on the robot

Own the research and development work through to the real-world experiments that prove feasibility and deployability

Investigate and shape the technical roadmap, aligning real-world needs with technical development

Mentor junior team members and collaborators

PhD in robotics, computer science, electrical engineering, aerospace engineering, mechanical engineering, or a related field, with publications at top venues such as RSS, CoRL, ICRA, IROS, T-FR, T-RO, CVPR, NeurIPS, or similar.

Research experience in active perception, informative path planning, vision-language models, or semantic mapping for exploration.

Hands-on work with vision-language or foundation models for robot perception, plus the physical principles governing them.

Fluency in Python and the ability to run your methods on real robots with C++ and ROS 2.

A principled fluency with uncertainty: Bayesian or information-theoretic planning, calibration, and conformal methods

Real-robot experience in the wild

Work on metric-semantic scene graphs, open-vocabulary mapping, or spatio-temporal scene representations.

Experience fine-tuning or deploying compact VLMs on edge computers

Exposure to robotic industrial inspection workflows