PhD Research Intern - Robotics & Physical AI

Palona; Los Altos; Sur place; EUR 13 000 - 20 000; Temps partiel

Résumé du poste

Palona is seeking a PhD Research Intern with a strong robotics background to prototype and evaluate the next stage of our Physical AI stack. You will work on connecting interaction understanding models to real robots in dynamic environments, with initial use cases in restaurants and healthcare where timely, context-aware decisions matter.

This hands-on research role blends frontier AI with practical robotics, aiming for robust perception, timing, and execution in real systems and potential

Qualifications

• Pursuing a PhD in a relevant field.
• Strong hands-on robotics experience in an academic or industrial lab.
• Experience programming, training, integrating, and debugging real robots.
• Familiarity with robotic perception, planning, control, or embodied AI systems.
• Strong software engineering skills in Python and C++.
• Experience with PyTorch is a plus.

Responsabilités

• Extend interaction understanding models for real-time robotic deployment decisions.
• Build closed-loop systems connecting perception, planning, robot execution, monitoring, and replanning.
• Develop methods to decide when, where, and how a robot should act based on physical-world events.
• Integrate and program real robotic platforms for research and pre-production experiments.
• Monitor robot execution, detect failures or deviations, and trigger rapid replanning and redeployment.
• Design experiments for constrained robotics tasks in restaurant and healthcare environments.
• Evaluate system robustness across changes in actors, objects, timings, and environments.
• Explore interaction-centric representations to support reliable robot behavior.
• Contribute to research publications, prototypes, and patentable innovations.

Connaissances

PhD candidate in Robotics

Robotics hands-on experience

Python & C++

PyTorch

ROS/ROS2

System integration

Formation

Ongoing PhD in Robotics, CS, AI, CV, ML

Outils

ROS/ROS2

PyTorch

Robot Operating Systems

Description du poste

Palona AI is building the operational intelligence layer for Physical AI.

A core part of our research is interaction understanding: treating interactions, rather than objects alone, as a first-class perception target. Our goal is to enable agentic systems that are not merely instruction-reactive, but event-proactive—systems that understand what is happening in the physical world, plan the right response, and deploy timely action.

We are now extending this capability into a closed-loop robotic system:

Perception → Interaction Understanding → Planning → Deployment → Execution Monitoring → Replanning

We are looking for a PhD Research Intern with a strong robotics background to help prototype and evaluate this next stage of our Physical AI stack.

The internship will focus on research and pre-production experiments that connect our interaction understanding models with real robots in dynamic environments. Initial use cases will span restaurants and healthcare, where timely, context-aware deployment decisions are critical.

This is a hands-on research role for someone who wants to work on problems that sit between frontier AI research and real-world robotics deployment.

You will have the opportunity to work on systems that are not limited to benchmark evaluation or simulation, but are designed to operate in real physical environments where perception, timing, reliability, and execution all matter.

What You’ll Work On

• Extend our interaction understanding models to support real-time robotic deployment decisions.
• Build closed-loop systems that connect perception, planning, robot execution, monitoring, and replanning.
• Develop methods for deciding when, where, and how a robot should act based on physical-world events and operational context.
• Integrate and program real robotic platforms for research and pre-production experiments.
• Monitor robot execution, detect failures or deviations, and trigger rapid replanning and redeployment.
• Design experiments for constrained but economically useful robotics tasks in restaurant and healthcare environments.
• Evaluate system robustness across changes in actors, objects, locations, timing, and environmental conditions.
• Explore how interaction-centric representations can support more reliable and generalizable robot behavior.
• Contribute to research publications, prototypes, and potentially patentable system innovations.

What We’re Looking For

• Currently pursuing a PhD in Robotics, Computer Science, AI, Computer Vision, Machine Learning, or a related field.
• Strong hands-on robotics experience, ideally through work in an academic or industrial robotics lab.
• Experience programming, training, integrating, and debugging real robots.
• Familiarity with robotic perception, planning, control, or embodied AI systems.
• Strong software engineering skills, preferably in Python and C++.
• Experience working with modern deep learning frameworks such as PyTorch.
• Ability to move fluidly between research ideas and working physical systems.

Preferred Qualifications

• Publications at top AI, computer vision, or robotics venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, RSS, CoRL, ICRA, or IROS.
• Experience with vision-language models, video understanding, interaction recognition, embodied AI, or robot learning.
• Experience with real-time perception and decision-making systems.
• Experience with ROS / ROS2, simulation environments, motion planning, or robotic middleware.
• Experience deploying models or robotics systems outside of purely simulated settings.
• Background or domain understanding in healthcare, assistive robotics, hospital operations, elder care, or clinical environments.
• Experience working with limited, noisy, or weakly supervised data.