> Markdown version of [/jobs/ext/3685887-applied-research-scientist-in-robot-learning-and-manipulation](https://www.wearedevelopers.com/jobs/ext/3685887-applied-research-scientist-in-robot-learning-and-manipulation). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Applied Research Scientist in Robot Learning and Manipulation

- **Company:** Eurecat Technology Centre
- **Location:** Barcelona, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Programming, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Tensorflow, Robotic Automation Software, Reinforcement Learning, Pytorch, Large Language Models, Embodied AI, Free and Open-Source Software
- **Published:** October 11, 2026
- **Apply:** https://www.adzuna.es/details/5921892595

## About the Role

Are you excited about building the next generation of robots that can learn, adapt, and act in the physical world?, We are looking for someone with PhD-level research maturity - either through a completed PhD or equivalent research experience - and hands-on experience applying AI/ML methods to real robotic systems., We do not expect you to match every keyword. We are looking for depth, curiosity, and the ability to turn research ideas into working robotic systems.

Essential

 * We are looking for someone with a strong academic background in robotics, AI, machine learning, or a related field, ideally currently completing or recently graduated from a Master's degree, and motivated to apply AI/ML methods to real robotic systems.

 * Experience with robot learning, manipulation, embodied AI, learning for control, or AI-based robotic perception/action.

 * Strong programming skills in Python.

 * Ability to work with real robotic systems, not only offline datasets or simulation.

 * Scientific and technical maturity: ability to define problems, test hypotheses, analyse results, and communicate findings.

 * Good written and spoken English.

Highly valuable

 * Experience with imitation learning, behaviour cloning, reinforcement learning, offline RL, diffusion policies, transformers, VLA models, or foundation models for robotics.

 * Experience with ROS/ROS 2.

 * Experience with PyTorch, TensorFlow, or similar frameworks.

 * Experience with robotic arms, mobile manipulators, force/contact-rich manipulation, bimanual manipulation, or human-robot collaboration.

 * Publications, open-source contributions, project demos, or research prototypes in robot learning or embodied AI.

 * Experience in European or collaborative R&D projects.

This role is probably a good fit if…

 * You have worked on AI methods that make robots perceive, decide, manipulate, adapt, or learn.

 * You enjoy both research and implementation.

 * You are comfortable moving between papers, code, experiments, robots, and project discussions.

 * You want to work in applied research: scientifically ambitious, but connected to real systems and industrial impact.

 * You are attracted by the opportunity to contribute to a European flagship effort in robotics foundation models.

## Description

You will contribute to applied research and development in areas such as:

 * Imitation learning, reinforcement learning, or related approaches for robotic manipulation and mobile manipulation skill acquisition.

 * Foundation models for robotics, including multimodal and vision-language-action approaches, that can generalise across tasks, objects, environments, or robotic platforms.

 * Integration and validation of AI methods on real robotic systems.

 * Human-robot collaboration and manipulation in industrial or semi-structured environments.

 * Scientific publications, project deliverables, demonstrations, and collaboration with European partners.

You will work on both simulations and real-world robotic systems, going beyond isolated academic benchmarks. The role focuses on developing innovative ideas, implementing them, testing them on real robots, and refining them to meet practical constraints.