PhD Student for Graph Neural Network Models for Sensori-Motor Cortex

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 to develop and analyze predictive machine learning models for sensori-motor cortex of freely behaving macaque monkeys. This project is a collarboration with the lab of Prof. Gail at the German Primate Center within the DFG funded CRC 1456 “Mathematics of Experiment” (100% E13 position).

The candidate is expected to develop novel graph based machine learning models for recorded neurons in sensori-motor cortex, analyze the trained models to better understand the frame of reference (coordinate system) used by populations of neurons during motor planning during natural behavior, and develop predictions that can be tested in subsequent experiments. The PhD position is purely computational, but the candidate is expected to closely collaborate with the experimentalists at the German Primate Center.

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.