PhD Student for Machine Learning Models for Mouse Superior Colliculus

How to apply

Who to contact?	sinz@cs.uni-goettingen.de
Email subject	Start with "[ML PhD]"
What to include?	All applications What degree do you (soon) hold (e.g. "Master in Applied Data Science")? Curriculum Vitae Statement of motivation (max 500 words) outlining your personal interest in this research area. Your latest transcript of records A short list of which relevant skills you have and which you would like to acquire. Links to code you have written (e.g. github or bitbucket) If applicable, at most two publications or pre-print you (co)-authored Known programming languages and experience Names and emails of two references. Possible start date If you apply with your own project idea Do you bring funding or not? A concise (pdf, max 1/2 page) “research proposal” on a project you would be interested working on. This is not binding but helps to get the conversation started.

Project Description

We are looking for a highly motivated and skilled PhD student (E13 100%) to develop and analyze predictive machine learning models for superior colliculus in mice. This project is a collarboration with Dr. Katrin Franke and Prof. Andreas Tolias at Baylor College of Medicine (BCM), Houston, Texas.

The candidate is expected to develop machine learning models superior colliculus and analyze the learned model w.r.t. functional properties that enable behavioral decisions in mice. The analyses should lead to predictions that can be tested in subsequent experiments by our collaborators. The PhD position is purely computational, but the candidate is expected to closely collaborate with the experimentalists at BCM Houston. To get and idea about the kind of work, you can read the latest publication in Nature.

The ideal candidate has a degree in machine learning, physics, math, electrical engineering, or related fields, and a strong background in mathematics, machine learning, or statistics, with prior experience in (computational) neuroscience.

Our international team puts great emphasis on high quality research in an open and collaborative research environment. We particularly encourage women and other underrepresented groups in STEM fields to apply. Please check the How to apply section below.