The Big Data in BioMedicine Group at the Chair of Experimental Bioinformatics (School of Life Sciences, Technical University of Munich (TUM)) invites applications for a postdoctoral position for the development of innovative algorithms for the joint analysis of single-cell & spatial transcriptomic data in the framework of the Novo Nordisk funded collaborative data science grant “MOPITAS” - Multi-omics profiling in time and space. The position is available for up to 4 years.
Project:
The role of cells in the tissue is largely defined by their differential gene expression, which unfolds both in space and in time. The methods recording this structural information, known as Spatial Transcriptomics (ST) suffer from low transcriptomic resolution, and are prohibitively expensive to perform at scale. Conversely, single-cell RNA sequencing is a mature, cost-effective technology that captures more RNA transcripts and identifies individual cells adequately. This transformative project seeks to bridge the gap between ST and scRNA-seq and to incorporate information on gene-regulatory activity via scATAC-seq. We will utilize novel and innovative algorithms based on a complex interplay of deep neural networks. The focus of TUM in this collaborative project will be to develop new computational methods and software tools to decipher location-specific gene-regulatory mechanisms, with the aim of unraveling how tissues form and change over time and space.
Environment:
The successful candidate will be part of a strong international consortium (University of Southern Denmark, Copenhagen University, EMBL, TUM) which covers a broad range of expertise ranging from data-science driven wet-lab experimentation to cutting-edge algorithm development. It is envisioned that the involved researchers are actively visiting the connected labs and foster international exchange.
TUM is one of the most highly ranked academic institutions in Germany. Within TUM, the candidate will be embedded in a young and dynamic research group (https://biomedical-big-data.de/) located at the School of Life Sciences in Freising. The candidate will benefit from a stimulating research environment that covers many aspects of systems biology ranging from studying gene-regulatory mechanisms to drug repurposing. The candidate will have access to state-of-the-art computing facilities for bioinformatics data processing and machine learning.
Requirements:
We are seeking outstanding candidates with a PhD in Computer Science, Bioinformatics, Molecular Biology or similar, with strong analytical and problem-solving skills, who are strong in written and oral communication (in English) and have experience in bioinformatics method development and analysis of single-cell sequencing and spatial transcriptomics data. Extensive knowledge of and practical skills in statistical analysis, data mining, data integration, machine learning and programming in R or python are a plus.
Application process:
Applicants should send a dossier containing a motivational statement (max. one page), a curriculum vitae summarizing qualifications and experience, a list of publications, a certified copy of a PhD degree certificate, names and the email addresses of at least one referee as a single PDF document to Dr. Markus List (via e-mail to markus.list [at]tum.de). Online interviews will be conducted with selected candidates and remaining shortlisted candidates may be invited to the TUM campus in Freising, Germany, for face-to-face meetings with the group. The deadline for applications is March 10th, 2023.
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Equal opportunity:
The Technical University of Munich is an equal opportunity employer. As such, applications from women are explicitly encouraged. Preference will be given to candidates with disabilities who have essentially the same qualifications.
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