Floral Traits Macroevolution Postdoc (ML/Phylogeny)

Visma Recruit; Stockholms kommun; On-site; SEK 420,000 - 540,000; Full time

Job summary

The Swedish Museum of Natural History in Stockholm invites applications for a two-year Postdoc focused on Floral Traits, Machine Learning and Macro-evolution. The project aims to study flower size evolution on oceanic islands by combining phylogenetic methods with ML-based trait data extraction from digitised floras.

You will develop computational workflows to create large-scale, standardised trait datasets and test hypotheses about drivers of evolution, using ML tools and macro-phylogenetic

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.
• 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.
• Own salary secured for the project.

Responsibilities

• Two-year Postdoc focusing on Floral Traits, Machine Learning and Macro-evolution.
• Develop computational workflows to generate large-scale, standardised datasets of floral traits.
• Test hypotheses about the predictability and drivers of flower-size evolution in island systems.
• Work with developing methods in ML and macro-phylogenetic tools to understand trait evolution on islands.

Skills

Phylogenetic comparative methods

Macro-evolutionary analyses

R programming

English proficiency

Plant biodiversity data

NLP / ML interest

Publications

Independent collaboration

Education

PhD in plant systematics or related field

Job description

The Swedish Museum of Natural History in Stockholm invites applications for a two-year Postdoc focused on Floral Traits, Machine Learning and Macro-evolution. The project aims to study flower size evolution on oceanic islands by combining phylogenetic methods with ML-based trait data extraction from digitised floras.

You will develop computational workflows to create large-scale, standardised trait datasets and test hypotheses about drivers of evolution, using ML tools and macro-phylogenetic