Background
Develop and evaluate Bayesian optimisation strategies to tune robot controllers with as few real-world trials as possible. This project sits within the machine learning and robotics activities of our group at ETH Zürich, and builds on several recent publications and ongoing collaborations with industry and other research groups.
Objectives
- Review the state of the art in Bayesian optimisation and formulate a precise research question
- Design and implement the methodology described above
- Evaluate results rigorously, including ablations and comparison with baselines
- Document the work in a thesis and, where results allow, a conference paper
What you will do
You will join weekly group meetings, work closely with a PhD student as day-to-day supervisor, and have access to our computing/lab infrastructure. We expect independent work, curiosity and good communication. The exact scope is adapted to a master thesis of about 6 months.
What we offer
- Supervision by Anna Müller and an experienced team
- Financial support (see funding details)
- An international environment and the possibility to continue with a PhD for strong candidates
How to apply
Apply through ThesisAbroad with your CV, transcript and a short motivation explaining why this topic interests you. Shortlisted candidates are invited to a 30-minute video call.
Requirements
- Enrolled in a master programme in Artificial Intelligence or a related field
- Solid background in machine learning
- English (B2 or higher)
- Programming or lab experience relevant to the topic
- Ability to spend the full duration on site