2-year DDLS Postdoc on Floral Traits, Machine Learning and Macro-evolution

NATURHISTORISKA RIKSMUSEET; Stockholms kommun; On-site; SEK 357,120 - 468,720; Full time

Job summary

The Swedish Museum of Natural History invites applications for a two-year Postdoc position in the Department of Bioinformatics and Genetics. The project focuses on Floral Traits, Machine Learning and Macro-evolution, aiming to model flower-size evolution on island systems using phylogenetic and ML approaches.

The successful candidate will develop computational workflows to generate standardized trait datasets and integrate macro-phylogenetic tools, with a preferred start date of 01-10-2026.

Qualifications

• 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.

Responsibilities

• Develop computational workflows to generate large-scale, standardised datasets of floral traits.
• Use these datasets to test hypotheses about the predictability and drivers of flower-size evolution in island systems.
• Integrate macro-phylogenetic tools to understand floral trait evolution on islands.
• Collaborate within the Department and develop methods in ML for trait data extraction from floras.

Skills

Phylogenetic methods

Macroevolutionary analyses

R programming

English proficiency

Education

PhD in plant systematics or related field

Tools

R programming

Job description

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.

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. The 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 on developing methods in ML and integrate macro‑phylogenetic tools to understand floral trait evolution on islands. The preferred start date is 01‑10‑2026.

Key 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.

Additional Experience 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.
• Less than three years since the doctoral degree was awarded (special circumstances may apply).

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, openness to new ideas, and the ability to communicate your ideas.

Employment Details

Form of employment: Temporary employment. Duration: Two years, commencement 2026‑10‑01. Candidates are required to secure their own external funding for the project. Salaries will be determined by the collective agreements RALS and RALST‑T.