2-year DDLS Postdoc on Floral Traits, Machine Learning and Macro-evolution
Core
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.
Role type
Postdoctoral researcher in macroevolution and machine learning
Builds
Large-scale, standardised datasets of floral traits and computational workflows for trait extraction
Domain
Botany, macroevolution, machine learning, phylogenetics
Deliverable
production ML models
Required skills
Phylogenetic comparative methods, macroevolutionary analyses, R programming, large biological dataset analysis
Preferred skills
Plant biodiversity data handling, digitised flora analysis, natural language processing, large language models, machine learning applied to biological data
Technologies
R, machine learning frameworks, phylogenetic software
Responsibilities
Develop computational workflows to generate standardised datasets of floral traits; integrate macro-phylogenetic tools to understand floral trait evolution on islands; test hypotheses about the predictability and drivers of flower-size evolution in island systems
Seniority
Postdoctoral researcher (early career)