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Postdoctoral Fellow
Postdoc in Machine Learning for Spatial Multi-omics of Glioblastoma Full-time
Job description:
The research group “Computational Genomics and Systems Genetics” of Prof. Dr. Oliver Stegle (https://www.dkfz.de/en/bioinformatik-genomik-systemgenetik/) is looking for a highly motivated postdoctoral fellow to join an ambitious project to explore how the tumour microenvironment (TME) drives malignant cell states in glioblastoma (GBM) using large-scale spatial multi-omics. Funded as part of the Wellcome Leap program, the project GBM-space will bring together internationally leading experts to integrate single cell transcriptomics, epigenomics and spatial RNA/DNA-sequencing to systematically deconstruct TME-GBM cell interactions and plasticity in situ.
Our research group has a track record in the development of tailored computational methods for high-throughput omics data, machine learning for multi-omics integration and spatial omics. The successful candidate will lead the computational aims the BGM-space project. A major opportunity within GBM-space is to develop novel analytical strategies and concepts for integrating spatial multi-omics datasets, thereby allowing to unravel intratumor heterogeneity in space.
You will be affiliated with the laboratory of Prof. Dr. Stegle and collaborate with the partners of the GBM-space project at the Wellcome Sanger Institute, Cambridge and the Crick. The position will also be connected to a vibrant local ecosystem for data science and machine learning, including the recently funded ELLIS Life Heidelberg unit. We seek to build on previous expertise and methods devised by our team (see below).
Recent publications of the Stegle research groups:
Kleshchevnikov, Vitalii, et al. “Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics.” bioRxiv (2020) & Nature Biotechnology, in press.
Velten, Britta, et al. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. bioRxiv (2020) & Nature Methods, in press.
Svensson, Valentine, Sarah A. Teichmann, and Oliver Stegle. SpatialDE: identification of spatially variable genes. Nature methods 15.5 (2018): 343-346.
Argelaguet, R., et al. Multi-Omics factor analysis disentangles heterogeneity in blood cancer. Molecular systems biology 14.6 (2018): e8124.
Requirements:
The successful applicant will hold a doctoral degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical development.
Previous experience in developing and applying statistical and/or machine learning-based computational methods to large real world datasets is expected. Expertise in analysis of omics data, genetics, statistical interpretation and analysis of next-generation sequencing datasets is beneficial, as is communicating results in scientific conferences and papers. We seek a highly motivated, creative, organized, and well-positioned team member to lead this scientific project at all stages.
We offer:
Interesting, versatile workplace
International, attractive working environment
Campus with modern state-of-the-art infrastructure
Salary according to TV-L including social benefits
Possibility to work part-time
Flexible working hours
Comprehensive further training program
Access to the DKFZ International Postdoc Program