For any application, please write to jobs-gagneurlab@in.tum.de 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 shareable code).

Postdoc

We have an open postdoc position for 2 years (core funding, flexible reaserch topic). Application dealine Nov 30th 2021. See details here. 

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. 

 

IDP, Guided research, 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: https://tinyurl.com/y7betk96.

Open projects

Apply to what you find most appealing to you. The objectives for most projects can be adjusted to fit an IDP,  a Guided research, a Bachelor or a Master thesis. You will typically be mentored by a PhD student or Postdoc working on the topic.  

  • 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 (https://www.mll.com/en/science/5000-genome-project.html).  
     
  • Algorithms for the 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. 
     
  • When the outlier is the signal: Integrative multi-omics analysis of Amyotrophic lateral sclerosis (ALS)
    Our lab won the Kaggle Challenge "End ALS" by combining AI methods for outlier detection and gene network analysis, yielding potential new genes involved in Amyotrophic lateral sclerosis. See  Task 1 at https://www.kaggle.com/alsgroup/end-als/discussion/242637.
    With this master thesis, you will analyse the complete AnswerALS dataset https://www.answerals.org/ to discover new potential targets and develop an integrative reproducible pipeline.     

  • Gene activation analysis
    We have already been successful in identifying outliers in gene expression. Our method, OUTRIDER, requires that the gene is expressed in most of the samples and fails in genes with a very low (or null) expression, which we usually discard. Nevertheless, it is well-acknowledged that activation of proto-oncogene drives carcinogenesis. The goal of this thesis is to develop a statistical method to properly capture cases of gene activation. ​​You will leverage a unique dataset of 5,000 leukemia transcriptomes from our collaborator MLL (https://www.mll.com/en/science/5000-genome-project.html). 

Open research assistant positions (HiWi)

Working as research assistant on a mini-job basis (between 8 and 20 hours a week) is an awesome way to develop your skills and contribute to the field very early in your carreer (after a couple of study semesters). Also, you and our team get to know each other, and you can start advancing on your future thesis.

  • We look for a HiWi to join our oncology team for variant annotation and cancer driver gene predictions using large datasets. You will learn how to deal with reproducible workflows, machine learning models and dealing with big data.
  • Further projects and  theses can be defined according to the skills and interests of the applicants.  Do not hesitate to contact us! 

Further bioinformatics projects in Munich

Check the bioinformatik-muenchen web site.