PhD Student (gn*) Computational Biology

Fixed-term of 3 years | Part-time with 65% (25 hours/week) | Salary according to TV-L E 13 | Lab for Functional Genomics in Psychiatry at the Department of Mental Health, University Muenster

We are UKM. We have a clear social mission and, with our focus on healthcare, research, and teaching, we bear a unique responsibility.

To meet our high standards every day, we are looking forward to your scientific support – ideally with you on board!

RESPONSIBILITIES:

The mission of the Ziller lab is to develop and apply novel strategies to dissect the genetic and epigenetic basis of complex diseases, with particular focus on psychiatric disorders. Our research is focused on the question how many genetic and environmental risk factors act in concert to create a permissive molecular environment that fosters the emergence of psychiatric disorders such as schizophrenia and bipolar disorder and lead to treatment resistance.

In order to address this problem, we employ a highly interdisciplinary, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics to patient level traits.  The Ziller lab is part of the Department for Mental Health of the Medical Faculty, University of Muenster.

The overall goal of the Department is to dissect the molecular mechanisms underlying psychiatric diseases and treatment resistance, rapidly utilizing these insights to develop new patient tailored therapeutic approaches in a knowledge driven fashion.

REQUIREMENTS:

We are looking for motivated individuals skilled in computer science/computational biology/bioinformatics with the desire to make a difference for people suffering from mental health problems. A Masters Degree in (Bio)Informatics/Data Science or related disciplines is desirable. The positions focus on the development and application of new methods to predict multi-omic traits and phenotypes from large genetic datasets. More specifically, the computational team will build on our previous work (PMID: 38951512) to establish and train deep neuronal network models on large existing dataset with multi-omic data. Subsequently, these models will be applied to clinical cohorts of individuals suffering from mental illness to perform patient stratification, discovery of new biological mechanisms and stratified drug target identification, empwering personalized medicine in psychiatry.  Experience in quantitative genetics and/or deep neural networks, R/Python/PyTorch/Tensorflow etc. is desirable.

Apply now via our career portal until 19.11.2025, including

• Cover letter expressing research interests
• Brief summary of your previous projects, including applied techniques
• Complete CV