Postdoc Position in Statistical Learning for Biomedicine

Postdoc Position in Statistical Learning for Biomedicine at Technische
Universität Darmstadt (3 Years, E14 Salary)

Job Description

Your task will be to develop ground-breaking robust statistical learning
methods for high-dimensional biomedical databases. The focus lies on
conducting fundamental research to derive scalable methods with
robustness and statistical guarantees, such as, false discovery rate
control. The developed methods are applied within an interdisciplinary
biomedical cooperative research project to discover novel and
reproducible biomarker signatures. Your research will enable a better
understanding of autonomic dysfunction, which is an important mechanism
for the development of atherosclerotic diseases and their consequences.
Furthermore, your research will be applied to extract clinically
interpretable and robust graph and nonlinear regression models from
high-dimensional multi-omics data.

Details below and available here:

Applicants should have, e.g., a PhD in mathematical statistics or in
electrical engineering with a specialization in signal processing and
statistical learning. You should have a track record in your research
field, as evidenced by publications in top quality journals.

Application Procedure
To apply for this position, please send your CV, a one-page personal
statement, full contact details of two referees, transcripts and
publications to and

Signal Processing Group of Prof. Abdelhak M. Zoubir
Robust Data Science Group of Prof. Michael Muma

Technische Universität Darmstadt

The position is part of the curATime project, funded by the German
Federal Ministry of Education and Research (BMBF) within the Clusters
for Future Framework.

Start date
March 2023

Closing Date
Applications are considered until the position is filled.

Statistical Learning, Signal Processing, Graph Learning,
High-Dimensional Data, False Discovery Rate (FDR) Control, Reproducible
Biomarker Discovery, Electrocardiogram and Multi-Omics Data,
Interdisciplinary Research

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