PhD Student (gn*) Computational Biology

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 of 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.

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.

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 Master’s Degree in (bio-)informatics / data science or related disciplines is desirable. The position focus lies 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 data sets 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, empowering personalized medicine in psychiatry. Experience in quantitative genetics and/or deep neural networks (R / Python / PyTorch / Tensorflow etc.) is desirable.

Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference.