PhD Student for Machine Learning in Parkinson's Disease

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 probabilistic machine learning methods for diseases factors and progression in Parkinson’s disease. This project is a collaboration with Dr. Kathrin Brockmann at the university hospital Tübingen and part of the Else Kröner Medical Scientist Kolleg ClinBrAIn. The candidate is expected to develop novel machine learning models to capture determining factors in the progression of Parkinson’s disease. The models will be based on three of the wordwide largest Parkonson cohorts. As part of the program, the PhD student will closely collaborate with medical doctors and get close insight into research and medical practice in neurodegenerative diseases.

The ideal candidate has a degree in machine learning, physics, math, electrical engineering, or related fields, and a strong background in mathematics, machine learning, statistics, and ideally causal learning, with prior experience in working on life science/clinical data.

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.