Postdoc – Machine Learning / Computational Biology

Postdoc – Machine Learning / Computational Biology

German Research Center (DKFZ)

Heidelberg, Germany

The German Cancer Research Center is the largest biomedical research institution in Germany. With approximately 3,000 employees, we operate an extensive scientific program in the field of cancer research.

The Division of Computational Genomics and System Genetics is seeking a

Postdoc – Machine Learning / Computational Biology
(Ref-No. 2019-0076)

Description:
The research group of Oliver Stegle at DKFZ in collaboration with the Wolfgang Huber group at EMBL, located in Heidelberg, work on machine learning and statistical computing solutions for cutting-edge biology and biomedicine research. Their interdisciplinary teams engage in theoretical method development, translation into effective software, and scientific applications in collaboration with researchers from fields including cancer and developmental biology.

We are looking for a senior scientists / postdoc with machine learning expertise who will develop methods for finding low-dimensional explanations in high-dimensional biological data. Biological systems can now be studied at multiple levels (DNA sequence, chromatin plasticity, transcriptome, proteome, metabolome, imaging, etc.), increasingly at single-cell resolution. Such data, which often comprise millions of features per observational unit, have a wide range of important applications in basic biology and in biomedicine. You will be working on the most fundamental step in their analysis: finding and understanding the most important patterns. These can come in the form of latent factors, clusters, smooth manifolds, graphs, etc. The learning tasks comprise unsupervised, supervised and hybrid setups.

This advertisement is for one of two positions in this new and exciting field, which can either focus on: (1) mathematical theory development or (2) development of high-quality scientific software and biological application in collaboration with domain experts.

Relevant publications:

Argelaguet, Ricard, Velten, B., et al. “Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets.” Molecular Systems Biology (2018), DOI 10.15252/msb.20178124.
Moore, R., Casale, F. P., et al. “A linear mixed model approach to study multivariate gene-environment interactions.” Nature Genetics (2019). DOI 10.1038/s41588-018-0271-0.

Your profile:
A master and a PhD or equivalent qualification in a quantitative science (mathematics, statistics, physics, computer science, computational biology) is required. For position (1), you will have excellent theoretical foundations in probability theory, statistics, linear algebra and differential geometry. For position (2), you have experience in statistical data analysis and a solid understanding of programming concepts and multiple languages including R and Python.

You are excited by making or contributing to biological discoveries, and you are interested in the modern biotechnologies that enable us to make the measurements with which you work. You will be involved in interdisciplinary research projects, therefore you should enjoy collaborative work.

Experience with biological or biomedical data is a plus.

The position is limited to 3 years with the possibility of prolongation.

The position can in principle be part-time.

For further information please contact
Dr. Oliver Stegle, phone +49 6221 42-3598.

The German Cancer Research Center is committed to increase the percentage of female scientists and encourages female applicants 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 (">www.dkfz.de*).

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 cause.

Please apply via recruiter’s website.

Quote Reference: Ref-No. 2019-0076

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