A PhD position is available in the Computational Biology group of the Technical University of Munich (Prof. Julien Gagneur) starting immediately.
You will develop computational methods and models expanding Kipoi, a collaborative initiative to define standards and to foster reuse of trained models in genomics . Kipoi builds on a 3-way collaboration with international partners (Stegle lab, EMBL Heidelberg, and Kundaje lab, Stanford). The Kipoi model repository at https://kipoi.org is increasingly used and extended by the research community.
Research topics include: expressive mathematical representations of RNA or protein encoded regulatory sequences using deep learning approaches (e.g. ); integrative models of individual steps of gene expression; methodologies for interpretability of deep learning models, and for their application to the prediction of causal effects of genetic variants in rare or common diseases (e.g. ). We expect applications on large-scale public data and on unpublished datasets from experimental collaborators in biology (e.g. ) or medicine (e.g. ).
Applicants must hold a master in bioinformatics, or in physics, statistics, or applied mathematics with a genuine interest in applications to genomics. (S)he should have know-how in machine learning or statistical modeling and demonstrated programming experience with R or python. (S)he should have excellent communications skills and work within an interdisciplinary setting.
The Gagneur lab is a lively, international, and interdisciplinary computational biology group with a research focus on the genetic basis of gene regulation and its implication in diseases. We are located in the informatics department of the Technical University of Munich, one of the top ranked European universities. Our lab has strong links to other local scientists and institutions in biology and medicine. Munich offers an outstanding, dynamic, interactive and internationally oriented research environment. Munich, the 2018 “most livable city in the world” according to the urban magazine Monocle, and the proximity of the Alps provide an excellent quality of life.
1. Avsec et al., Kipoi: accelerating the community exchange and reuse of predictive models for genomics, bioRxiv, 2018
2. Avsec et al., Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks, Bioinformatics, 2017
3. Cheng et al., Modular modeling improves the predictions of genetic variant effects on splicing, bioRxiv, 2018 – winner model of the CAGI 2018 splicing challenge
4. Schwalb et al., TT-seq maps the human transient transcriptome, Science, 2016
5. Kremer et al., Genetic diagnosis of Mendelian disorders via RNA sequencing, Nature communs, 2017