Visit acad.jobs with all Jobs for Academics!
                    
Position: Postdoc - Computational Biology for Genetic Disorders
Institution: Technische Universität München
Location: München, Bayern, Germany
Duties: You will develop computational methods and analyse multi-omics datasets (genome, transcriptome, proteome, metabolome) to unravel the genetic and molecular basis of genetic disorders. Your research topics include: Detection of aberrant molecular events in multi-omics dataset, generalizing our software OUTRIDER for RNA-seq to multiple omics data modalities; development of variant and gene prioritization algorithms by integrating multi-omics data together with deep learning models of regulatory variants, leveraging the model repository of machine learning models for genomics Kipoi; Integration of multi-omics with wearable sensor data in the context of a new research network with Stanford. You will work directly on patient data
Requirements: Applicants must either hold a PhD in computational biology, or hold a PhD in physics, statistics, or applied mathematics with practical experience with real world high-dimensional data analysis. Applicants with a PhD in biology with strong quantitative skills and demonstrated experience with genetics and analysis of sequencing data will also be considered. The candidate must have a proven publication record, interest for translational research, and have demonstrated the ability to work independently and creatively
   
Text: Post-doc position in Computational Biology for Genetic Disorders 29.10.2018, Wissenschaftliches Personal Your role You will develop computational methods and analyse multi-omics datasets (genome, transcriptome, proteome, metabolome) to unravel the genetic and molecular basis of genetic disorders. Your research topics include: Detection of aberrant molecular events in multi-omics dataset, generalizing our software OUTRIDER for RNA-seq to multiple omics data modalities [1]; development of variant and gene prioritization algorithms by integrating multi-omics data together with deep learning models of regulatory variants, leveraging the model repository of machine learning models for genomics Kipoi (https://kipoi.org, [2]); Integration of multi-omics with wearable sensor data in the context of a new research network with Stanford (Lars Steinmetz lab). You will work directly on patient data, in tight collaboration with two close collaborators: Dr. Holger Prokisch, who is coordinating the European consortium for mitochondrial disorder GENOMIT (example collaboration [3]), and Prof. Christoph Klein, head of the Children’s Hospital of the University of Munich (example collaboration [4]). You are Applicants must either hold a PhD in computational biology, or hold a PhD in physics, statistics, or applied mathematics with practical experience with real world high-dimensional data analysis. Applicants with a PhD in biology with strong quantitative skills and demonstrated experience with genetics and analysis of sequencing data will also be considered. The candidate must have a proven publication record, interest for translational research, and have demonstrated the ability to work independently and creatively. (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, communicating with biologists and geneticists. We are 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. Apply The position is funded from core funding with a salary according to the TV-L (German academic salary scale). 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 “Postdoc-rare18” in the title. More https://www.gagneurlab.in.tum.de https://kipoi.org 1. Brechtmann et al., OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data, AJHG, in press and bioRxiv 2. Avsec et al., Kipoi: accelerating the community exchange and reuse of predictive models for genomics, bioRxiv, 2018 3. Kremer et al., Genetic diagnosis of Mendelian disorders via RNA sequencing. Nature communications, 2017 4. Witzel et al., Chromatin remodelling factor SMARCD2 regulates transcriptional networks controlling early and late differentiation of neutrophil granulocytes, Nature Genetics, 2017 Hinweis zum Datenschutz: Im Rahmen Ihrer Bewerbung um eine Stelle an der Technischen Universität München (TUM) übermitteln Sie personenbezogene Daten. Beachten Sie bitte hierzu unsere Datenschutzhinweise gemäss Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. Durch die Übermittlung Ihrer Bewerbung bestätigen Sie, dass Sie die Datenschutzhinweise der TUM zur Kenntnis genommen haben. Kontakt: gagneur@in.tum.de, cc: weise@in.tum.de
Please click here, if the Job didn't load correctly.







Please wait. You are being redirected to the Job in 3 seconds.