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 (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

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.
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.
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.
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 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.

Evidence of Exceptional Impact

Candidates who demonstrate one or more of the following will receive strong consideration:

High-impact publications or research recognized by the robotics, AI, or computer vision community.
Open-source tools, datasets, or benchmarks adopted beyond the candidate's immediate research group.
Leadership in major robotics projects or interdisciplinary teams.
Patents, technology transfer, or successful deployment of learning-based robotic systems.
Clear evidence of translating research ideas into reliable physical-robot capabilities.

Application Materials

Curriculum vitae and complete publication list.
Two or three representative papers, with a short statement of the candidate's contribution for collaborative work.
Research statement of no more than two pages, including future research directions relevant to Physical AI.
Links to robot demonstrations, project pages, open-source repositories, code, or datasets.
Optional: a brief portfolio summarizing real-robot systems personally designed, implemented, or evaluated.

Candidate Screening Guidance

Screen for demonstrated research originality, technical depth, experimental rigor, and direct ownership of real-robot work. Strong candidates should be able to explain what they personally contributed, why the problem mattered, how the method advanced the state of the art, how it behaved on physical hardware, and what evidence supports robustness and generalization. Do not treat venue names alone as sufficient evidence of fit.

Work Environment

The position requires regular access to robotics laboratory facilities and close collaboration across research and engineering disciplines. The exact balance of on-site and hybrid work will depend on laboratory needs and applicable organizational policy.

Relocation Supported: Yes

Visa Sponsorship Approved: No

Senior Researcher (Physical AI and Robot Learning)