Postdoc in Computational Population Genetics

DTU Health Tech seeks qualified candidates for a postdoc position in computational population genetics with a potential start date around December 1, 2021 (or according to mutual agreement).


In this position, you will develop algorithms and computational methods to deal with the analysis of large datasets from modern and ancient sources. More specifically, these algorithms will be aimed at analyzing a large number of ancient genomes using population genetics methods. Additional information should be obtained by contacting the potential main supervisor directly. The university is located in the greater Copenhagen area, which is acknowledged for its excellent standards of living, childcare and welfare system.

Current computational methods are often ill-equipped to deal with DNA extracted from ancient populations. This ancient DNA shows high levels of fragmentation and accumulated chemical damage. Furthermore, the number of individuals that can be sequenced is often limited. Fortunately, several problems pertaining to ancient DNA and ancient paleogenetics can be described in a maximum-likelihood framework and computer science techniques can help us to solve such numerical problems efficiently via machine learning, numerical algorithms and data structures. You will work in collaboration with other partners including the University of Copenhagen in order to develop the next generation of algorithms and software applied to DNA from fossils which can then be used to reconstruct population history and infer selection.

Given the COVID19 pandemic, we will happily accommodate requests for remote work until in-person work is deemed safe.


You must hold a PhD degree (or equivalent) ideally in biological science with a focus on quantitative and mathematical aspects, or in computer science or mathematics.

The candidate we are looking for should ideally have the following qualifications:

  • Knowledge of a programming language like Python, Perl, C++ and/or Java
    • Ability to work in a UNIX environment, ideally in a high-performance computing environment
    • Ideally, proficiency in C/C++ or Java or similar is a plus (not required)
    • Thorough understanding of basic principles of population genetics
    • Knowledge of probabilities and statistics
    • Firm grasp of first-year university mathematics (differential calculus/linear algebra)
    • Knowledge of coalescence theory or diffusion theory is an advantage
    • Expertise in next-generation sequencing data generation and processing is also a plus

The language of communication at DTU is English.

More information and how to apply here

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