Log In
Sign Up
Postdoctoral Fellow
Postdoc in Machine Learning for Decoding Cellular Networks Full-time
Job description:
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 EMBL Heidelberg.
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): 304-312.
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.
Gagneur, Julien, et al. Genotype-environment interactions reveal causal pathways that mediate genetic effects on phenotype. PLoS Genet 9.9 (2013): e1003803.
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