Applications
Bachelor’s and master’s theses
If you are interested in doing your bachelor’s or master’s thesis research in our lab, please read the instructions on our teaching page.
Prospective PhD students and postdocs
PhD students should apply through the ELLIS PhD program. The application deadline is usually in November for starting dates between April and September in the following year. Only in very rare cases do we accept exceptionally talented PhD students outside this application cycle.
Postdoc candidates can apply at any time.
Our lab is part of a large collaborative network with partners in the US and Germany. The projects are funded by the European Research Council through an ERC Starting Grant and in tight collaboration with Andreas Tolias' lab at Stanford. Regular visits at Stanford are possible and encouraged.
Your profile: Background in systems / computational neuroscience or machine learning (deep learning in particular). Experience with both is a strong plus, but not a requirement.
If you want to work with us on exciting projects developing machine learning methods for neuroscience, please email Alexander Ecker and make sure of the following:
- Tell us why you are interested in joining our lab specifically and what you would like to work on.
- Provide the names and email addresses of one or two people who would be willing to provide a reference letter for you.
- Don’t forget to attach a CV.
Example projects
A foundation model of the brain
The Enigma Project @stanford is a large-scale, interdisciplinary initiative dedicated to building a foundation model of the brain at single-neuron resolution. Combining large-scale electrophysiology and naturalistic experiments with modern, multi-modal deep learning, Enigma aims to uncover core principles of neural representation and intelligence – starting with our richest sense: the visual system. Our lab in Göttingen has pioneered large-scale predictive models of the visual system and will continue to develop the next generation of predictive models with a focus on using them to gain insights into brain function.
Role: Postdoc (preferred) or PhD student
Data-driven multi-modal discovery of cell types in the neocortex
Understanding the relationship between structure and function of cortical neurons and circuits is one of the key challenges in neuroscience. In this project, we develop deep learning methods for data-driven identification of excitatory cell types in the visual cortex and to understand how a neuron’s morphology relates to its function. We will harness a unique large-scale functional anatomy dataset: a combination of electron-microscopy reconstructions at sub-micrometer resolution with two-photon functional imaging of nearly all excitatory neurons in one cubic millimeter of the mouse visual cortex.
Role: Postdoc (preferred) or PhD student
