Senior Researcher (Physical AI and Robot Learning)

Job description

Job Description Senior Researcher (Physical AI and Robot Learning) Job Location: Bangalore Location Flexibility: Primary Location Only Req Id: 12041 Posting Start Date: 9/3/26 Embodied Intelligence | World Models | Task Planning | Real-Robot Deployment LOCATION Bengaluru, India EMPLOYMENT TYPE Full-time WORK MODE On-site / Hybrid, with regular robotics laboratory access POSITION FOCUS Research excellence and deployable Physical AI About the Role Fujitsu Research India is building a Physical AI Lab to create robots that can perceive, reason, plan, learn, and act reliably in complex real-world environments. We are seeking an exceptional researcher with a PhD and a strong record of advancing robotics, artificial intelligence, machine learning, or embodied intelligence through rigorous research and physical-robot validation. The successful candidate will shape novel research in robot learning and control while also connecting perception, task planning, world models, and action execution into dependable end-to-end robotic systems. This role is intended for researchers who can convert ambitious scientific ideas into reproducible, measurable, and deployable capabilities. Core hiring principle: Require broad familiarity across reinforcement learning, control, task planning, VLA models, and world models; expect genuine research depth in at least two areas; keep evidence of physical-robot implementation mandatory. Key Responsibilities 1. Research and Algorithm Development • Conduct world-class research in Physical AI, embodied intelligence, robot learning, planning, and autonomous decision-making. • Develop novel methods in reinforcement learning, imitation learning, learning-based control, model-based RL, vision-language-action models, world models, and long-horizon task planning. • Design agents that connect semantic reasoning and task planning with motion generation, control, and closed-loop execution. • Advance robustness, safety, generalization, adaptation, and data efficiency for robots operating under real-world uncertainty. 2. Physical-Robot Deployment and Evaluation • Train, evaluate, and deploy policies on physical robots; simulation-only experimentation is not sufficient for the role. • Address sim-to-real transfer, system identification, domain randomization, online adaptation, latency, sensing uncertainty, and failure recovery. • Design rigorous experiments, benchmarks, ablations, and evaluation protocols that measure task success, reliability, safety, and generalization. • Produce reproducible research assets, including well-engineered code, experiment documentation, datasets, and robot demonstrations. 3. End-to-End Physical AI Systems • Integrate perception, state estimation, semantic understanding, task planning, motion planning, learning, control, and safety mechanisms into complete robotic systems. • Collaborate closely with researchers in AI, computer vision, controls, planning, systems, and robotic hardware. • Contribute to scalable robot-data collection and learning pipelines for manipulation, mobile manipulation, locomotion, or multi-robot settings. 4. Scientific and Strategic Impact • Publish influential research at premier AI, computer vision, and robotics venues. • Generate patents and contribute to technology transfer, open-source software, datasets, and benchmark development as appropriate. • Mentor junior researchers, support research strategy, and communicate technical results clearly to both expert and business stakeholders. • Build collaborations with leading academic and industrial research groups. Required Qualifications - Non-Negotiable Education and Research Excellence • Completed PhD, or PhD expected before joining, in Robotics, Artificial Intelligence, Machine Learning, Computer Science, Electrical Engineering, Control, Mechanical Engineering, or a closely related discipline. • Proven publication record, including meaningful first-author contributions at leading venues such as ICRA, IROS, RSS, CoRL, CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, IEEE Transactions on Robotics, IEEE Robotics and Automation Letters, Science Robotics, Nature Machine Intelligence, or comparable venues. • Ability to identify important research problems, formulate technically sound solutions, and evaluate them with scientific rigor. Real-Robot Evidence • Demonstrated implementation and evaluation on physical robots, supported by publications, project pages, videos, open-source code, datasets, competition results, or deployment outcomes. • Hands-on ability to diagnose the gap between algorithmic performance in simulation and behavior on real hardware. • Simulation-only experience is insufficient. Technical Depth • Research depth in at least two of the following: reinforcement learning, imitation learning, robot control, task planning, motion planning, world models, VLA models, foundation models for robotics, embodied AI, or multi-robot systems. • Working familiarity with the broader Physical AI stack, including perception, planning, learning, control, system integration, safety, and evaluation. • Strong programming ability in Python and PyTorch or JAX, together with practical experience in C++, ROS/ROS2, and robotics simulation platforms such as MuJoCo, Isaac Sim, or Isaac Lab. • Strong experimental methodology, problem-solving ability, technical writing, and verbal communication. Preferred Experience • Robotic manipulation, bimanual or dexterous manipulation, mobile manipulation, locomotion, or human-robot collaboration. • Offline RL, diffusion policies, behavior cloning, self-supervised learning, model-based RL, or test-time adaptation. • Long-horizon task planning, hierarchical policies, memory-augmented agents, neuro-symbolic reasoning, or closed-loop replanning. • Multimodal foundation-model pre-training or post-training, vision-language models, VLA