About the Role (Senior Researcher - Physical AI) :

Fujitsu Research India (FRIPL) 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, or a closely related discipline.

- 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 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.

- Industrial robotics applications in manufacturing, logistics, inspection, service robotics, or warehouse automation.