Job Location: Bangalore

Location Flexibility: Primary Location Only

Req Id: 13630

Posting Start Date: 10/28/26

Fujitsu's R&D vision is to create cutting-edge technologies that support society and prioritize the flow of data. Our five key technologies are essential elements for collecting data from all parts of society, transporting it over ultra-high-speed and secure networks, analyzing it with trusted AI, converting it into value, and returning it to society. Central to realizing our strategy and vision are the people behind our R&D initiatives. We strive to foster top-notch talent in the innovation process by nurturing skilled individuals equipped to advance this cause with originality and dedication.

Website: https://www.fujitsu.com/global/about/research/

Researcher – Physical AI and Robot Learning

About The Role

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 connecting perception, task planning, world models, and action execution into dependable end-to-end robotic systems. The role is intended for researchers who can convert ambitious scientific ideas into reproducible, measurable, and deployable capabilities.

Core Hiring Principle

Broad familiarity across reinforcement learning, control, task planning, VLA models, and world models
Genuine research depth in at least two areas
Evidence of physical-robot implementation is mandatory

Key Responsibilities

into complete robotic systems.

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 for this role.
Address:

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:

Perception
State estimation
Semantic understanding
Task planning
Motion planning
Learning
Control
Safety mechanisms

Collaborate closely 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, as appropriate
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

Required Qualifications

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

Candidates should have 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
Multi-robot systems

In Addition

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

Evidence of Exceptional Impact

Strong consideration will be given to candidates demonstrating one or more of the following:

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

Candidates should demonstrate research originality, technical depth, experimental rigor, and direct ownership of real-robot work. Strong candidates should be able to explain their personal contribution, why the problem mattered, how their method advanced the state of the art, how it behaved on physical hardware, and what evidence supports robustness and generalization.

Application Materials

Candidates Are Requested To Provide

Curriculum vitae and complete publication list
Two or three representative papers, with a short statement explaining 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: Brief portfolio summarizing real-robot systems personally designed, implemented, or evaluated

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 & Robot Learning