For any application, please write to including your CV, transcripts if you're studying, and a brief yet convincing motivation relating your interest to specific research topics of us (either from our previous publications or projects described here). Moreover, provide your wished starting and end dates. Possibly, provide evidence of your programming skills (e.g. github repos, or by sending sharable code).

PhD theses

Graduate School Munich School of Data Science (MuDS)

We are a core lab of MuDS, a graduate school to promote Data Science and its application. MuDS offers joint projects for PhD students, each designed by two partners – a domain-specific application partner and a methodological partner. This ensures that candidates receive methodological as well as application-specific training. Check the MuDS web site for application schedule (Feb 28 in 2019).

Graduate School of Quantitative Biosciences Munich (QBM)

We are proud members of QBM, a graduate school funded by the German excellence initiative to promote quantitative biosciences. Students selected by QBM will get their own stipend, extra training from the graduate school, and conduct interdisciplinary research under the direction of two labs with complementary expertise. Check the QBM web site for application schedule and specific projects. 


HiWi, Bachelor, and Master theses

We are constantly seeking for highly motivated students in bioinformatics, physics and/or applied mathematics for Master thesis. Quantitative minds with a strong interest for biology, or biologists with computational skills and eager to understand biology at the genome level will fit our team. See this video for an overview of our research and projects:

Open Master projects:

  • Multi-tissue modeling of gene expression from DNA sequence by deep learning
    The goal of this master thesis is to develop deep learning models that model the regulatory code and its modulation across tissues. A focus will be given on modeling human promoters.
  • Prediction of cancer driver genes
    The goal of this master thesis is to to develop novel machine leanirng models integrating DNA and RNA-sequencing data to identify novel cancer driver genes. You will leverage a unique dataset of 5,000 genomes and transcriptomes from our collaborator MLL (  
  • When the outlier is the signal: detection of aberrant expression as causes of rare disease
    The goal of these two companion master theses is to develop algorithms to identify aberrant gene expression events in omics dataset. The first thesis focuses on single-cell RNA-sequencing data. The challenge is the very high dimensionality of the data and the difficult nature of the noise (low counts). The second thesis focuses on proteomics data where the difficulty lies in the handling of "missing not random" data. The methods find application to identify causes of rare diseases. Techniques include: machine learning (autoencoders), statistical modeling. 

Further master or bachelor theses can be defined according to the skills and interests of the applicants. Also we offer mini-jobs ("HiWi") on these topics: A great way to already contribute to the field, get to know each other, and start advancing on your future theses. Do not hesitate to contact us! 

Further bioinformatics projects in Munich

Check the bioinformatik-muenchen web site.