PhD position in Machine Learning for Genomics

Type de poste
Dates
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Localisation
Adresse

Munich
France

Contacts
Julien Gagneur
Email du/des contacts
gagneur@in.tum.de
weise@in.tum.de
Description

<p>A PhD position is available in the Computational Biology group of the Technical University of Munich (Prof. Julien Gagneur) starting as soon as possible.</p><p><strong>Your role</strong></p><p>You will develop computational methods and models expanding Kipoi (http://kipoi.org), a collaborative initiative to define standards and to foster reuse of trained models in genomics [1]. 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. Your research topics include: expressive and effective mathematical representations of RNA or protein encoded regulatory sequences, notably using deep learning approaches (e.g. [2]); development of integrative models of individual steps of gene expression (transcription, splicing, RNA degradation, translation and protein degradation); development of 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. [3]). We expect applications on large-scale public data as well as on unpublished datasets from experimental collaborators in biology (e.g. [4]) or medicine (e.g. [5]).</p><p><strong>You are</strong></p><p>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 be able to articulate clearly the scientific and technical needs, set clear goals and work within an interdisciplinary setting.</p><p><strong>We are</strong></p><p>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 &ldquo;most livable city in the world&rdquo; according to the urban magazine Monocle, and the proximity of the Alps provide an excellent quality of life. Apply The position is funded from core funding with a salary according to the TV-L (German academic salary scale). We encourage joining the graduate school QBM (Quantitative Bioscience Munich). Applications including a cover letter, CV, and references must be sent to Julien Gagneur (gagneur@in.tum.de, cc: weise@in.tum.de) until Nov 30th 2018 referring to &ldquo;PhD-Kipoi18&rdquo; in the title.</p><p><strong>More</strong></p><p>https://www.gagneurlab.in.tum.de<br />https://kipoi.org<br />https://qbm.genzentrum.lmu.de<br />1. Avsec et al., Kipoi: accelerating the community exchange and reuse of predictive models for genomics, bioRxiv, 2018<br />2. Avsec et al., Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks, Bioinformatics, 2017<br />3. Cheng et al., Modular modeling improves the predictions of genetic variant effects on splicing, bioRxiv, 2018 &ndash; winner model of the CAGI 2018 splicing challenge<br />4. Schwalb et al., TT-seq maps the human transient transcriptome, Science, 2016<br />5. Kremer et al., Genetic diagnosis of Mendelian disorders via RNA sequencing, Nature communs, 2017</p><br/>
Laboratoire: Gagneur lab, Computational Biology, Technical University of Munich