Biodiversity Forecasting & Vegetation Dynamics Scientist

Aarhus University; Aarhus; On-site; DKK 420,000 - 540,000; Full time

Job summary

Aarhus University, Department of Biology, invites applications for a two-year postdoctoral researcher position in biodiversity forecasting, vegetation dynamics and rewilding. The project, part of the Biodiversa+ REDESIGN initiative, aims to develop spatially explicit forecasts of how depopulation, climate and land-use change may reshape European plant diversity and ecosystem functioning.

The successful candidate will assemble and harmonise large datasets, develop forecasts to 2100 (and 2300

Qualifications

• PhD in ecology, macroecology, biogeography, environmental science, geography, ecological modelling, ecoinformatics or related field.
• Experience with biodiversity modelling and forecasting.
• Ability to work in an interdisciplinary team.
• Strong quantitative and programming skills.

Responsibilities

• Assemble, harmonise and analyse large biodiversity datasets.
• Develop forecasts of vegetation and biodiversity under climate and depopulation scenarios.
• Implement SDMs or dynamic vegetation models.
• Quantify uncertainty and conduct sensitivity analyses.
• Link forecasts to functional traits and ecosystem indicators.
• Collaborate with partners across Europe and contribute to publications.

Skills

Spatial ecological modeling

Species distribution models

R and Python programming

Geospatial data handling

Climate and land-use scenarios

Remote sensing experience

Scientific writing

Education

PhD in ecology or related field

Tools

R

Python

GIS

Job description

Aarhus University, Department of Biology, invites applications for a two-year postdoctoral researcher position in biodiversity forecasting, vegetation dynamics and rewilding. The project, part of the Biodiversa+ REDESIGN initiative, aims to develop spatially explicit forecasts of how depopulation, climate and land-use change may reshape European plant diversity and ecosystem functioning.

The successful candidate will assemble and harmonise large datasets, develop forecasts to 2100 (and 2300