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.
Skills Required
- PhD in robotics, computer science, electrical engineering, aerospace engineering, mechanical engineering, or a related field
- Publications at top venues such as RSS, CoRL, ICRA, IROS, T-FR, T-RO, CVPR, NeurIPS, or similar
- Research experience in scene understanding, semantic SLAM, lifelong or spatio-temporal mapping, world models, or embodied reasoning
- Hands-on experience applying vision-language or foundation models to spatial reasoning, memory, or long-horizon embodied question answering
- Expertise in 3D representations, including scene graphs, neural or Gaussian-splat maps, or object-centric maps
- Strong Python programming skills
- Ability to run methods on real robots using C++ and ROS 2
- Ability to define meaningful representations and evaluation methods
- Change detection or long-term mapping results from real deployments
- Experience with Real2Sim, interactive system identification, or digital twins
- Experience with behavior trees or language-model-driven task planning
- Industrial or field robotics experience
What We Do
FieldAI is pioneering the development of a field-proven, hardware agnostic brain technology that enables many different types of robots to operate autonomously in hazardous, offroad, and potentially harsh industrial settings – all without GPS, maps, or any pre-programmed routes.







