The Ziller lab at the Department of Mental Health, University of Muenster, is recruiting a PhD student for a fixed-term, part-time position focused on computational biology in psychiatry. The lab's mission is to develop and apply innovative strategies to unravel the genetic and epigenetic foundations of complex psychiatric diseases, such as schizophrenia and bipolar disorder, with an emphasis on understanding treatment resistance. Research in the lab employs an interdisciplinary approach, integrating human pluripotent stem cell-based models, high-throughput functional genomic screening, and big data-driven machine learning to bridge the gap from genetic variation to patient-level traits. The successful candidate will join a computational team tasked with developing and applying new methods to predict multi-omic traits and phenotypes from large genetic datasets. Building on previous work (PMID: 38951512), the team will establish and train deep neural network models using extensive multi-omic data, which will then be applied to clinical cohorts of individuals with mental illness. The goals include patient stratification, discovery of novel biological mechanisms, and identification of stratified drug targets to advance personalized medicine in psychiatry. Applicants should hold a Master's degree in (Bio)Informatics, Data Science, Computer Science, Computational Biology, or a related field, and have experience with quantitative genetics and/or deep neural networks. Proficiency in R, Python, PyTorch, or Tensorflow is desirable. The position offers a fixed-term contract for three years, part-time at 65% (25 hours/week), with salary according to TV-L E 13. The department is committed to rapidly translating molecular insights into patient-tailored therapeutic approaches. Applications should include a cover letter detailing research interests, a summary of previous projects and techniques, and a complete CV. The deadline for applications is November 19, 2025. For more information and to apply, visit the provided application link.