Ph3Overview /h3pExperteer Overview /ppIn this role, you will lead high-fidelity robotics simulations and validation workflows to ensure real-world performance of robotic systems. You’ll collaborate across hardware, AI, and simulation teams to build custom sensor models and synthetic data pipelines. You’ll advance physics-informed ML and mechanical simulations to improve realism and predictive accuracy at the edge. This position offers the chance to shape state-of-the-art simulation-driven validation for intelligent edge robotics. /ph3Benefits /h3ulliDesign and implement custom sensor models (ToF, RGB-D, LiDAR, IMUs) in simulation platforms (NVIDIA Isaac Sim) using USD integration /liliApply FEA-based mechanical modelling with tools like Ansys, Comsol, or Abaqus to simulate stress and dynamics /liliLead sim-to-real alignment through domain randomization, noise modelling, and physics-based constraints /liliDevelop synthetic data generation workflows for training perception, control, and physics-informed ML models /liliCollaborate with AI/ML engineers to integrate simulation outputs into model training pipelines /liliOversee field validation and on-site testing of robotic platforms and sensors to ensure fidelity and real-world performance /liliMentor junior engineers and drive adoption of advanced simulation/validation techniques /liliStay current with robotics simulation advances and sensor tech /li /ulh3Responsibilities /h3ulliPh.D. or M.S. in Robotics, Mechanical Engineering, Computer Science, Physics, or a related field /lili7+ years of robotics simulation, sensor modelling, or mechanical validation experience /liliDeep expertise in simulation platforms (e.g., Isaac Sim, Gazebo) and sensor modelling /liliStrong proficiency in Python and C++ /liliHands-on experience with FEA tools (Ansys, Comsol, Abaqus) /liliExperience with ML frameworks (PyTorch, TensorFlow) and scientific computing libraries /liliExperience with synthetic data generation and validating against physical benchmarks /liliSolid understanding of sim-to-real challenges and mitigation strategies /li /ulh3Qualifications /h3ulliPh.D. or M.S. in Robotics, Mechanical Engineering, Computer Science, Physics, or a related field /lili7+ years of robotics simulation, sensor modelling, or mechanical validation experience /liliDeep expertise in simulation platforms (e.g., Isaac Sim, Gazebo) and sensor modelling /liliStrong proficiency in Python and C++ /liliHands-on experience with FEA tools (Ansys, Comsol, Abaqus) /liliExperience with ML frameworks (PyTorch, TensorFlow) and scientific computing libraries /liliProven track record of integrating simulation with real-world robotic systems /liliExperience with synthetic data generation and validating against physical benchmarks /liliSolid understanding of sim-to-real challenges and mitigation strategies /li /ul /p #J-18808-Ljbffr