Senior Researcher

About the Role

Fujitsu Research India (FRIPL) is establishing a Physical AI Lab focused on developing robots capable of perception, reasoning, planning, learning, and acting reliably in complex real-world environments. This role is for a researcher with a PhD and a demonstrated history of advancing robotics, artificial intelligence, machine learning, or embodied intelligence through rigorous research and physical-robot validation. The successful candidate will lead novel research in robot learning and control, integrating perception, task planning, world models, and action execution into dependable end-to-end robotic systems. The role emphasizes transforming ambitious scientific ideas into reproducible, measurable, and deployable capabilities.

Key Responsibilities

• 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 (VLA) models
• World models
• 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.
• Physical-Robot Deployment and Evaluation:
• Train, evaluate, and deploy policies on physical robots. Simulation-only experimentation is not sufficient.
• Address challenges such as:
• Sim-to-real transfer
• System identification
• Domain randomization
• Online adaptation
• Latency
• Sensing uncertainty
• Failure recovery
• Design rigorous experiments, benchmarks, ablations, and evaluation protocols to measure task success, reliability, safety, and generalization.
• Produce reproducible research assets including well-engineered code, experiment documentation, datasets, and robot demonstrations.
• End-to-End Physical AI Systems:
• Integrate systems including: Perception, State estimation, Semantic understanding, Task planning, Motion planning, Learning, Control, Safety mechanisms.
• Collaborate with researchers across 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.
• 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.
• Mentor junior researchers and support research strategy.
• Communicate technical results clearly to expert and business stakeholders.
• Build collaborations with leading academic and industrial research groups.

Requirements & Tech Stack

• Education and Research Excellence:
• Completed PhD, or PhD expected before joining, in: Robotics, Artificial Intelligence, Machine Learning, Computer Science, Electrical Engineering, 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:
• Robot control
• VLA models
• Foundation models for robotics
• Embodied AI
• Multi-robot systems
• Working familiarity with the broader Physical AI stack covering perception, planning, learning, control, system integration, safety, and evaluation.
• Strong programming ability in Python and PyTorch or JAX.
• Practical experience with 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

• Experience in one or more of the following areas is preferred:
• 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 systems, or LLM-guided robotic reasoning.
• Sim-to-real transfer, domain randomization, system identification, uncertainty estimation, safety-critical autonomy, or failure recovery.
• Large-scale robot datasets, distributed training, scalable evaluation infrastructure, or real-time robotic systems.