MSc Thesis Internship: Robot Learning with Human Neural Supervision

We are looking for a Master’s student to conduct their thesis within our prototyping and applied systems team, contributing to research at the intersection of robot learning, multimodal data, and brain–computer interfaces. Robot demonstrations capture what a person does, but not necessarily when they recognize an error, reconsider an action, or initiate a correction. We investigate whether EEG-derived annotations can identify demonstration segments that are particularly useful for training autonomous robots. Working closely with researchers and engineers, you will tackle a focused validation question: **can neural-informed selection or weighting of demonstration data improve robot policy performance or reduce the amount of training data required?** The project combines egocentric or teleoperation recordings, framewise behavioral annotations, and densely sampled mental-state classifier outputs. Your work will focus on downstream robot-learning experiments, with support from our neuroengineering team for interpreting the neural signals. The role is intended to align with a Master’s thesis, subject to topic fit and university supervision. **Start:** By agreement, as soon as possible **Duration:** 4–6 months **Availability:** 10-20 hours per week **What you will do** * Define a tractable research question and evaluation protocol with the team * Prepare aligned demonstration data and annotations for robot-policy training * Implement methods for selecting or weighting demonstrations using neural and behavioral information * Compare against random selection and non-neural baselines, potentially including policy-influence methods * Train and evaluate robot policies in simulation and/or on available hardware * Assess task success, generalization, and data efficiency using controlled experiments and statistical analysis * Document reproducible experiments and contribute to your thesis and potential research publications The thesis will address one well-defined validation step rather than require building the complete recording, neural-analysis, and robot-learning pipeline. **What we offer** * Hands-on research connecting human neural signals with robot learning * Close collaboration with BCI researchers and applied-systems engineers * The opportunity to develop a rigorous thesis with practical relevance * A pragmatic, low-hierarchy environment with room to take ownership * Flexible scheduling around exams and coursework * Support for scientific writing and potential dissemination of the results **What you bring:** * Current enrollment in a Master’s program in Robotics, Computer Science, Machine Learning, AI, or a related field * Strong interest in robot learning, imitation learning, or learning from demonstrations * Solid Python programming skills and experience with a machine-learning framework such as PyTorch * Familiarity with training and evaluating machine-learning models * An understanding of experimental design, data leakage, and meaningful baseline comparisons * Ability to translate an open research question into a manageable implementation and evaluation plan * Comfortable working independently while collaborating across disciplines **Nice to have:** * Experience with imitation learning, reinforcement learning, or vision-language-action models * Familiarity with robot-learning tools such as LeRobot, RoboMimic, or simulation environments * Experience with multimodal time-series data, dataset curation, or data valuation * Experience running reproducible experiments across multiple training seeds * Exposure to EEG or brain–computer interfaces; prior neuroscience experience is not required * Prior involvement in research projects, open-source contributions, or scientific writing