Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Niche robotics PhD requirement and mandatory real-robot experience reduce applicant density despite Bangalore location.
PhD-level robotics research and real-robot deployment make skills highly domain-specific and less transferable.
PhD, strong publication record, and mandatory real-robot evidence make screening highly stringent.
Lead research and development in Physical AI focusing on robot learning, control, and integrated robotic systems with end-to-end real-world deployment.
Design, train, evaluate, and deploy reinforcement learning and planning-based algorithms on physical robots, ensuring robustness, safety, and generalization in real environments.
Drive scientific impact through publications, patents, mentoring, and collaboration while delivering reproducible and measurable robotic capabilities.
PhD completed or expected before joining in Robotics, AI, Machine Learning, CS, EE, Control, Mechanical Engineering, or closely related field.
Proven research excellence demonstrated by first-author publications in top AI, robotics, or computer vision conferences or journals.
Hands-on experience with implementation and evaluation on physical robots; simulation-only experience is insufficient.
Strong programming skills in Python, PyTorch or JAX, C++, ROS/ROS2, and familiarity with robotics simulation platforms (e.g., MuJoCo, Isaac Sim). Work Experience Required: Not explicitly mentioned in the JD.
Expertise with substantive research depth in at least two areas from reinforcement learning, robot control, task planning, world models, foundation models for robotics, or embodied AI, coupled with physical robot deployment experience.
Experience integrating perception, planning, learning, control, and safety into reliable, deployable robotic systems operating in real-world settings.
Evidence of scientific leadership such as high-impact publications, technology transfer, open-source contributions, and the ability to translate research into robust physical-robot capabilities.