Position: Postdoc in Machine Learning for Spatial Multi-omics of
Department: Computational Genomics and Systems Genetics
Code number: 2021-0379
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 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):
Argelaguet, R., et al. Multi-Omics factor analysis disentangles
heterogeneity in blood cancer. Molecular systems biology 14.6 (2018):
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.
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: 01.02.2022
Duration: The position is limited to 3 years with the possibility of
The position can in principle be part-time.
Application deadline: 16.12.2021
Eva Sabine Blum
Phone +49 6221/42-3601
Please note that we do not accept applications submitted via email.
The DKFZ is committed to increase the proportion of women in all areas and
positions in which women are underrepresented. Qualified female applicants are
therefore particularly encouraged to apply.
Among candidates of equal aptitude and qualifications, a person with
disabilities will be given preference.
To apply for a position please use our online application portal
(https: // www. dkfz.de/en/stellenangebote/index.php).
We ask for your understanding that we cannot return application documents that
are sent to us by post (Deutsches Krebsforschungszentrum, Personalabteilung,
Im Neuenheimer Feld 280, 69120 Heidelberg) and that we do not accept
applications submitted via email. We apologize for any inconvenience this may