Robotics Applied Scientist, Spatio-Temporal Scene Understanding & Memory
About this role
What You Will Get To Do • Investigate the appropriate representation for the robot's mission memory: a persistent spatio-temporal representation of what it saw, where, when, and how it changed, across missions and sites, so assets that move, disappear, or degrade are tracked rather than rediscovered.
• Build world models that let the robot reason and plan over that memory and recover physical parameters through interaction for physics-aware digital twins. • Combine learned and physically grounded models, with geometry and dynamics as the ground truth. • Bridge the perception-reasoning gap for long-term autonomy.
What You Have • 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, on scene understanding, semantic SLAM, lifelong or spatio-temporal mapping, world models, or embodied reasoning.
• Hands-on experience with vision-language or foundation models applied to spatial reasoning, memory, or long-horizon embodied question answering. • Depth in 3D representations: scene graphs, neural or Gaussian-splat maps, or object-centric maps, and the estimation underneath them. • Strong Python; able to run your methods on real robots with C++ and ROS 2. • Clear thinking about what a representation is for and how to evaluate it, and comfort with a fast-paced environment where research questions are set by what the next deployment needs.
What Will Set You Apart • Change detection or long-term mapping results from real deployments.
• Real2Sim, system identification through interaction, or digital-twin work. • Experience with behavior trees or task planning driven by language models. • Industrial or field robotics exposure.