Phd Student (gn*) Computational Biology

Münster, NW, DE, Germany

Job Description

Job Id: 11308

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

BENEFITS:




Exciting projects


Further education and training


Interdisciplinary collaboration


Be part of the research innovation


Even more benefits


(*gn=gender neutral)


More Information


Questions about how to apply? Click here



Equal opportunities are a part of our Human Resources Policy. UKM supports the compatibility of work and family life and is certified as "Family-friendly Company" since 2010. 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. Due to legal requirements working with us is only permitted with complete vaccination against measles.



Universitätsklinikum Münster


Albert-Schweitzer-Campus 1 | 48149 Münster | www.ukm.de

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Job Detail

  • Job Id
    JD3754732
  • Industry
    Not mentioned
  • Total Positions
    1
  • Job Type:
    Part Time
  • Salary:
    Not mentioned
  • Employment Status
    Permanent
  • Job Location
    Münster, NW, DE, Germany
  • Education
    Not mentioned