Thesis supervision

Introduction

Due to the high demand and the close integration of students into our research programme, the number of thesis students in the lab is limited. We use a structured application procedure to make sure that we can offer the best supervision possible for projects that are an ideal fit to the candidate’s background and skillset.

The lab operates largely in two domains: advanced analyses of brain data such as electrophysiology and fMRI, and computational modelling, including the development, training and analysis of deep neural networks. Please indicate in your application which overall direction you are more interested in.

Useful skillset

• Very good coding skills in Python
• Knowledge of machine learning and data science
• Basic knowledge of visual neuroscience and linear algebra
• Ideally: successful participation in ML4CCN or other coursework offered by the lab

Application procedure

1. Contact Tim Kietzmann to express your interest. Include a short motivation text and your transcript, or a list of relevant coursework.
2. Once a semester, the team meets to discuss applications. Eligible students are notified via email.
3. Later that month, candidates meet the team for a joint Thesis Fair afternoon, where available topics are presented in depth.
4. Once a match is found, the onboarding process begins.
5. Students meet their supervisor regularly, take part in lab meetings and become members of the research team.