Position: Postdoc in Machine Learning for Multi-Omics Profiling of Tumor Inception
Department: Computational Genomics and Systems Genetics / Regulatory Genomics and Cancer Evolution
Code number: 2021-0188
The German Cancer Research Center is the largest biomedical research institution in Germany. With more than 3,000 employees, we operate an extensive scientific program in the field of cancer research.
The research groups of Prof Dr. Oliver Stegle (https://www.dkfz.de/en/bioinformatik-genomik-systemgenetik/) and Dr. Duncan Odom (https://www.dkfz.de/en/regulatorische-genomik/index.php) are looking for a postdoctoral fellow to join an ambitious collaborative project to decipher clonal dynamics in cancer by integrating AI-based analytics with large-scale spatial transcriptomics analyses.
Our research groups combine internationally leading excellence in single cell and functional genomics technologies (Odom) with computational innovations in multi-omics integration, causal inference and machine learning (Stegle). The fellow will work in a highly multidisciplinary setting between both laboratories at the German Cancer Research Center (DKFZ) in Heidelberg. This project will generate novel insights into how clonal competition across space and time can shape tumor inception and cancer development in vivo.
You will be jointly affiliated with the laboratory of Prof. Dr. Stegle and Dr. Odom to experimentally design and execute experiments to study in vivo cancer clonal evolution using spatial transcriptomics and single-cell multi-omics analyses as well as to computationally develop tailored machine learning and causal inference methodologies to analyse these datasets. For these activities, we will build on prior computational and experimental expertise our laboratories (see below for relevant prior work).
Recent publications of the Stegle & Odom research groups:
- Velten, Britta, et al. Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO. bioRxiv (2020).
- 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.
- S. Aitken, C. Anderson, et al. Pervasive lesion segregation shapes cancer genome evolution. Nature (2020) 265-270.
- C Martinez-Jimenez, N. Eling, et al Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science (2017) 1433-1436.
The successful applicant will hold a doctoral degree or equivalent qualification in biological or computational sciences. Previous experience in developing and applying statistical and/or machine learning-based computational methods to large real world datasets is expected. Deeper expertise in computer science, statistics, mathematics, physics, and/or engineering would be highly desirable. 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.
- 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
Earliest possible start date: 01.09.2021
Duration: The position is initially limited to 2 years with the possibility of prolongation.
The position can in principle be part-time.
Application deadline: 10.08.2021
Dr. Oliver Stegle
Phone +49 (0)6221/42-3598
Please note that we do not accept applications submitted via email.