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University of Freiburg

PostDoc in Forest Floor Modeling, Remote Sensing, and AI at the University of Freiburg University of Freiburg in Germany

Degree Level

Postdoc

Field of study

Computer Science

Funding

Full-time postdoctoral position (100%) funded by the DFG research group. Initially 2 years with performance-dependent extension for another 2 years (total project duration 4 years). Salary according to TV-L E13; the post notes a current gross amount of more than €66,000 for full-time employment.

Deadline

Sep 24, 2026

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Country

Germany

University

University of Freiburg

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Keywords

Computer Science
Environmental Science
Agriculture
Biology
Remote Sensing
Artificial Intelligence
Earth Science
Rainforest Ecology
Soil Chemistry
Ecological Modeling
Statistics
Cambio Climático
ML

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About this position

Postdoctoral position at the University of Freiburg in the interdisciplinary DFG research group “Forest Floor”, focused on the modeling and projection of humus layer properties and dynamics in forests.

The project combines soil ecology, ecosystem modeling, remote sensing, machine learning, and artificial intelligence. The successful candidate will further develop and validate the mechanistic soil carbon model Yasso, apply explainable AI methods, integrate satellite/remote sensing and inventory data, and analyze the spatiotemporal variability of humus-layer properties and soil carbon under current and future climate conditions in Germany.

The role is part of a large collaborative research network involving more than 20 scientists from several universities and non-university partner institutions. The postdoc will publish results in international journals, present at conferences, and contribute to DFG reporting and collaboration with project partners.

Eligibility highlights: completed PhD in forestry, geoecology, environmental science, geoinformatics, ecosystem modeling, or a related field; strong experience in process-based soil or ecosystem modeling; experience with data-driven modeling and/or machine learning; experience with remote sensing and/or inventory data; strong Python/R skills; understanding of soil biogeochemistry and the forest carbon cycle; excellent English; and an independent, team-oriented working style.

Funding and contract: full-time TV-L E13 position, initially for 2 years with possible extension for another 2 years (project duration 4 years). The post mentions a current gross salary of more than €66,000 for full-time employment.

Application deadline: 24 September 2026. Apply with a cover letter, CV including publication list, and contact details of two referees in one PDF via the University of Freiburg portal (reference number 5172), or send the application to [email protected].

Funding details

Full-time postdoctoral position (100%) funded by the DFG research group. Initially 2 years with performance-dependent extension for another 2 years (total project duration 4 years). Salary according to TV-L E13; the post notes a current gross amount of more than €66,000 for full-time employment.

What's required

Completed PhD in forestry, geoecology, environmental science, geoinformatics, ecosystem modeling, or a related field; sound knowledge and proven experience in process-based soil or ecosystem modeling; experience in data-driven modeling and/or machine learning; experience processing and analyzing remote sensing and/or inventory data; very good programming skills for data analysis and modeling (e.g. Python, R); understanding of soil biogeochemistry and the carbon cycle in forest ecosystems; publications in international journals related to modeling; very good spoken and written English; interest in interdisciplinary collaboration and an independent, structured working style.

How to apply

Prepare a single PDF containing a cover letter, CV with publication list, and names/contact details of two referees. Apply via the University of Freiburg online portal using reference number 5172, or send the application electronically to [email protected]. Applications are reviewed from 24 September 2026 onward.

More information can be found here

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