Postdoctoral researcher to the Department of Bioinformatics and Genetics

The Swedish Museum of Natural History is a government agency with a mandate to promote knowledge, research and interest in our world. It is a prominent research institution and Sweden's largest museum. For more than 200 years, the museum has been collecting specimens and data and conducting research on life on earth. The collections contain more than 11 million plants, animals, fungi, environmental samples, minerals and fossils. All research and knowledge are shared in the exhibitions, Cosmonova and in activities at the museum and digitally.

In the Department of bioinformatics and genetics, we are using information technology and genetic methods to study biological diversity and evolution. We are now looking for a postdoc focused on probabilistic machine learning and probabilistic programming for evolution and biodiversity. The position will be placed in the research group led by professor Fredrik Ronquist (https://ronquistlab.github.io/). The group has a long track record of developing statistical analysis software for phylogenetics and related areas.


WORK TASKS
You will work within a highly collaborative, interdisciplinary team aiming to develop the next generation of tools for probabilistic inference in evolution and biodiversity. The work is funded by the Swedish Research Council, the Knut and Alice Wallenberg Foundation, and the Swedish Foundation for Strategic Research, among others. The team includes computational biologists, computer scientists and evolutionary biologists at KTH Royal Institute of Technology, BI Norwegian Business School, Université Claude Bernard Lyon 1 and the Swedish Museum of Natural History. The general goal is to separate model specification from the implementation of the inference machinery through universal probabilistic programming (see https://www.nature.com/articles/s42003-021-01753-7). This paves the way for the development of probabilistic machine learning, which extends partially specified models through the application of generative methods to large datasets. The team is about to release the first version of the TreePPL platform (https://treeppl.org), which represents an important first step in this direction. The successful candidate will work with evolutionary biologists and biodiversity researchers in developing and implementing probabilistic models in TreePPL that address challenging scientific problems in areas such as host-parasite evolution, diversification, online tree inference or species circumscription. In particular, we expect the candidate to extend the TreePPL inference machinery to support efficient inference for these models, using novel inference strategies or novel combinations of current techniques like Markov chain Monte Carlo, sequential Monte Carlo, and parallel tempering. We also expect the candidate to extend the framework with generative machine learning capabilities. The work will involve documentation of the platform, teaching at user workshops, and other outreach activities. The project runs until 2026-12-31, and if successful, can be extended further.

QUALIFICATIONS
We are looking for a candidate with a PhD in a relevant field, such as computational statistics, computer science or bioinformatics. For a position as postdoctoral research fellow, the degree should have been completed no more than three years before the closing date (excluding any period of parental leave, sick leave or equivalent). We will also consider applicants with a PhD from more than three years ago. Experience of postdoctoral research would be advantageous. We expect you to have a solid background in and experience of advanced modelling and statistical analysis, including the design or development of new models or inference techniques. We also expect some previous exposure to probabilistic programming and machine learning algorithms. Experience with modeling and analysis of relevant problems, such as inference of phylogeny from genetic data or analysis of environmental DNA data, would be advantageous. Similarly, previous experience with machine learning would be an asset. The work requires excellent skills in written and oral communication in English.

We are looking for a candidate with strong analytical and communicative skills, and who is results-oriented.

OTHER
We advance our knowledge of the natural world, inspiring to better care of our planet. Our ambition is that the employees of The Swedish Museum of Natural History shall represent the diversity in Sweden and we welcome every applicant.

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