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Position CRC1644/Z21– postdoc or PhD student Lippert group

The successful candidate will work 40 hours per week (100%), employed at the Hasso Plattner Institute gGmbH. The position is classified within remuneration group 13 of the collective wage agreement among the German states (TV-L). The earliest possible starting date is 1 April 2024 and the position is limited until 31 December 2027. The fixed term of employment is in accordance with Section 2 subsection 1 of the German Act on Limited Scientific Contracts (Wissenschaftszeitvertragsgesetz or WissZeitVG).

Your field of work:

This project will develop novel methodology and software for methodology for discovery of plasticity genes via QTL and GWAS analyses. The methods build especially on linear mixed models for testing of gene-by-environment interactions and multivariate traits [Lippert et al. 2014, Casale et al. 2015].

The Lippert group is developing methods at the interface of machine learning and statistics that enable novel analyses in computational biology. Statistical methods developed by Lippert and colleagues have been shaping the state of the art for advanced statistical modeling of phenotype data in quantitative genetics and genomics for over a decade. Examples of methods include FaST linear mixed models for correction of genetic relatedness in GWAS [Lippert et al. 2011, Listgarten et al. 2012], deep-learning-based statistical tests for genetic studies of imaging traits [Kirchler et al., 2020, Kirchler et al., 2021], linear mixed models for testing of rare genetic variants [Monti et al. 2022, Listgarten et al. 2013].

Your responsibilities:

  • Develop novel statistical methodology for discovery of plasticity genes via QTL and GWAS analyses
  • Develop Linear Mixed Models for the analysis of high-dimensional output variables
  • Develop statistical software for genome-wide association studies in python or R
  • prepare efficient, reusable, documented code for reproducible analyses
  • Collaborate with computational researchers and plant biologists in interdisciplinary research
  • Together with the other postdoc in the project develop and implement a research data management plan that integrates in the activities of NFDI DataPlant

Results from your work on this project should form the basis for building the next stage of your academic qualification.

Your qualifications:

Please see here for mandatory requirements for this position:

Prior specialization in plant sciences is helpful, but not required. Practical knowledge in any of the following areas is desirable:

  • Numerical computation in Python or R
  • Tensorflow or Pytorch
  • Applied linear algebra and multivariate calculus
  • statistics
  • quantitative genetics
  • bioinformatics
  • computer science
  • mathematics

What we offer:

The attractive and stimulating position in a vibrant, collaborative, and engaging HPI community offers abundant opportunities to cultivate your research, to exchange ideas with our carefully selected group of students, to contribute to our vision and growth, and to enjoy a great place to live – especially for families. HPI’s campus is spacious, green, modern, equipped with the latest technology – and right next door to Berlin. We also help you to accelerate your spin-off ideas with design thinking and our support program for entrepreneurs.

Please see here for working conditions at the Hasso Plattner Institute. See Employment at the Hasso-Plattner-Institute for Digital Engineering

How to apply:

Please follow the instructions here:

The deadline for applications is 31 January 2024.


Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011). FaST linear mixed models for genome-wide association studies. Nature methods8(10), 833-835.

Listgarten, J., Lippert, C., Kadie, C. M., Davidson, R. I., Eskin, E., & Heckerman, D. (2012). Improved linear mixed models for genome-wide association studies. Nature methods9(6), 525-526.

Monti, R., Rautenstrauch, P., Ghanbari, M., James, A. R., Kirchler, M., Ohler, U., Konigorski, S., & Lippert, C. (2022). Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes. Nature communications13(1), 5332.

Listgarten, J., Lippert, C., & Heckerman, D. (2013). FaST-LMM-Select for addressing confounding from spatial structure and rare variants. Nature genetics45(5), 470-471.

Kirchler, M., Khorasani, S., Kloft, M., & Lippert, C. (2020). Two-sample testing using deep learning. In International Conference on Artificial Intelligence and Statistics (pp. 1387-1398). PMLR.

Kirchler, M., Konigorski, S., Norden, M., Meltendorf, C., Kloft, M., Schurmann, C., & Lippert, C. (2022). transferGWAS: GWAS of images using deep transfer learning. Bioinformatics38(14), 3621-3628.

Lippert, C., Casale, F. P., Rakitsch, B., & Stegle, O. (2014). LIMIX: genetic analysis of multiple traits. BioRxiv, 003905.

Casale, F. P., Rakitsch, B., Lippert, C., & Stegle, O. (2015). Efficient set tests for the genetic analysis of correlated traits. Nature methods12(8), 755-758.