Position: Postdoc in Machine Learning for Decoding Cellular
Department: Computational Genomics and Systems Genetics
Code number: 2021-0380
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 Oliver
Stegle (https: // www. dkfz.de/en/bioinformatik-genomik-systemgenetik/) is
looking for a postdoctoral fellow to join an ambitious collaborative project
to decipher molecular networks by integrating AI-based analytics and causal
inference with high-throughput tissue-target perturbation genetics in order to
decode cellular networks. Funded as part of the ERC Synergy Project DECODE,
this role will involve a close collaboration between partners at DKFZ, EMBL
and Heidelberg University.
Our research group has an established track record in the development of
tailored computational methods for high-throughput omics data, machine
learning for multi-omics integration and causal discovery. The successful
candidate will have significant autonomy to develop innovative strategies for
causal discovery based on high-throughput omics and imaging assays. A unique
opportunity within the DECODE project is to integrate modelling output and
subsequent experimental design decisions.
You will be affiliated with the laboratory of Dr. Stegle and collaborate with
the partners of the DECODE project at EMBL, DFKZ and Heidelberg University.
The position will also be connected to a vibrant local ecosystem for data
science and machine learning in Heidelberg, including the recently founded
ELLIS Life Heidelberg unit. We seek to build on previous expertise and methods
devised by our team (see below). The position will be primarily based at
DKFZ Heidelberg but you will also be connected to the group's activities at
Recent publications of the Stegle research groups:
Velten, Britta, et al. Identifying temporal and spatial patterns of
variation from multi-modal data using MEFISTO. bioRxiv (2020) & Nature
Methods, in press.
Jerber, Julie, et al. Population-scale single-cell RNA-seq profiling
across dopaminergic neuron differentiation. Nature genetics 53.3 (2021):
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):
Gagneur, Julien, et al. Genotype-environment interactions reveal causal
pathways that mediate genetic effects on phenotype. PLoS Genet 9.9
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