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.
1 job(s).
WORK TASKS
The Department of Bioinformatics and Genetics at the Swedish Museum of Natural History is offering a two-year Postdoc position focused on Floral Traits, Machine Learning and Macro-evolution.
This project aims to investigate the evolution of flower size on oceanic islands by combining phylogenetic comparative methods with machine learning-based extraction of trait data from digitised botanical floras. The successful candidate will develop computational workflows to generate large-scale, standardised datasets of floral traits and use these to test hypotheses about the predictability and drivers of flower-size evolution in island systems.
As a postdoc in this project, you will work with developing methods in ML, integrate macro-phylogenetic tools to understand floral trait evolution on islands.
The preferred start date is 01-10-2026.
QUALIFICATIONS
Qualifications:
- completed a PhD in plant systematics or a related field
- Demonstrated expertise in phylogenetic comparative methods and macroevolutionary analyses.
- Strong programming skills in R and experience analysing large, complex biological datasets.
- Excellent proficiency in English, both spoken and written. (The research environment is international, therefore English is required.
In addition, experience in the following areas will be considered:
- Experience with plant biodiversity data, digitised floras, and/or trait databases.
- Interest in natural language processing, large language models, or machine learning applied to biological data.
- At least one publication in an international, peer-reviewed journal
- At the final application date, no more than three years shall have passed since the doctoral degree was awarded, or since a foreign degree deemed equivalent to a doctoral degree was obtained. If there are special reasons, an earlier degree may also be considered meritorious. Special reasons include leave due to illness, parental leave, elected positions within trade unions, service within the national defence, or other similar circumstances, as well as clinical service or other assignments relevant to the subject area
Personal competences
- Analytical thinking, creativity and the capacity to imagine novel solutions and take initiative.
- Ability to collaborate and to carry out research independently
- Good communication skills, ability to keep an open mind and communicate your ideas.
The selection of eligible candidates will be based on their potential to benefit from the training. The following criteria will be used to assess this potential: documented knowledge in a relevant research field, proficiency in written and spoken English, analytical thinking skills, ability to collaborate, as well as creativity, initiative, and independence. The evaluation will consider previous experience and academic performance, the quality of the degree project, references, relevant experience, interviews, and the candidates written motivation for applying. You have to have your own salary secured.
Form of employment: Temporary employment.
Duration: , commencement: 2026-10-01 To be considered for this position, you are required to have your own external funding for the project.
To be considered for this position, you are required to have your own external funding for the project.
Please include the following documents with your application:
- CV summarizing your education, positions, and academic work, including scientific publications.
- A Research plan (max. 4 pages), including:
- Main objectives, background, methodology, impact, and future potential of the project.
- Description of data-driven methods used and/or developed within the project.
- Budget, time plan, project risks, and mitigation plan.
- Proposal abstract (300 words)
- Names and contact details of 2-3 references
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.
We apply an individual, differentiated and objective salary-setting system in accordance with the collective agreements RALS and RALST-T.
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