Beschreibung

Who we are:

In ECOSENSE we explore new ways to quantify ecosystem processes and stress impact of climate change by developing novel sensor principles and measurement methods (https://uni-freiburg.de/ecosense/). We established a comprehensive infrastructure and sensor network in a nearby forest including three canopy access towers.

For our second phase starting this July we are seeking 15 doctoral researchers from environmental and engineering sciences. ECOSENSE offers highly interdisciplinary research and training in a cooperative and vivid working environment.

The project C4 aims to identify hot moments and hot spots of forest dynamics within ECOSENSE. We focus on combining all measurements with process and empirical modeling by calibrating models to measured physiological process variables, environmental drivers, and ecosystem productivity. Furthermore, we intend to evaluate existing process models and improve them using our novel high-resolution measurements of many processes.

The position is located in the group of Biometry and Environmental System Analysis (Prof. Carsten Dormann), which closely collaborates with the groups for Biogeochemical Systems Modelling (Prof. Rene Orth) and Sensor-based Geoinformatics (Prof. Teja Kattenborn) in this project.

Your Task:

Within ECOSENSE, you will combine process knowledge, empirical data, and machine learning to improve dynamic forest modeling for prediction and inference. You will contribute to the development of a novel hybrid modeling framework (Forest Informed Neural Networks, FINN) by developing, training, and testing a hybrid forest growth model based on ECOSENSE data (Photosynthesis, Sap Flow, GPP, Volatile Organic Compounds (VOCs), soil variables, remote sensing, tree growth, etc.). Your main objective will be to balance process based understanding and empirical models (in particular Deep Neural Networks) to develop a model of tree growth across multiple scales. In addition, you will integrate your model in FINN (Pichler & Käber, 2025) to better understand the underlying mechanisms within the context of a dynamic forest model and compare it with the other models (e.g. QUINCY). We expect from our candidate the preparation of scientific publications of our results and methodological advancements.

For implementing the Deep Neural Networks, you will use the R-Package cito, which provides an user-friendly interface and only requires very little knowledge of Machine Learning. However, we expect a high level of motivation to familiarize with Machine Learning and ecological process modeling.

Your qualifications and contributions to ECOSENSE:

·You hold a Master’s degree in environmental sciences, forest science, biology, computer sciences, or any other field related to modeling, machine learning, or ecology. If already available, please include your M.Sc. thesis in your application.

·Basic knowledge of tree physiology and plant ecology.

·Familiarity with fundamental concepts of forest dynamics such as succession trajectories and demographic processes.

·Some experience of process modeling, ideally with forest models, and model evaluation.

·Basic knowledge of Statistical Methods (e.g., Generalized Linear Models, Bayes’, Process Modeling) and experience in one programming language commonly used in data science (e.g. R and/or Python).

·Ability to harmonize and analyze diverse data sets.

·Ability to work independently and strong communication skills for collaborating with other ECOSENSE researchers.

·Excellent English skills.

·Great enthusiasm for working in a large interdisciplinary project and interest in collaborative research.

·Driver’s license is a plus.

What we offer

·An exciting interdisciplinary topic with a high impact socio-ecological impact

·A salary according to TV-L E13 (75%)

·Contracts will initially run for three years with an option of extension until the end of the CRC’s second phase, which will be June 2030.

·Modern laboratory equipment and a highly qualified, multicultural team, which will cooperate with you and support you along your professional growth.

The work on your dissertation will be strengthened by the CRC’s own integrated Research Training Group (RTG) providing a tailored qualification program for all our doctoral candidates. Within this RTG all early career researchers will form a tight group of scientists being connected across the borders of disciplines fostering a constant and mutual exchange.

The funding of the here advertised position is still subject to the DFG’s final approval for a second phase by midst of May 2026.

For any questions regarding the position, please contact Dr. Yannek Käber (yannek.kaeber@biom.uni-freiburg.de).

The position is initially a 3-year fixed-term contract with the possibility of extension until June 30, 2030. The salary will be determined in accordance with E13 TV-L.

We will be particularly pleased to receive applications from women for the position advertised here.

Bewerbung

Please send your application in English including supporting documents citing the reference number 00004901, by 15. April 2026 at the latest. Please send your application to the following address in written or electronic form:

Please upload your application document to the application portal of the University of Freiburg. For further information, please contact Dr. Yannek Käber on the phone number +49 761 203 8670 or E-Mail yannek.kaeber@biom.uni-freiburg.de.

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Doktorand*in (m/f/d) in Hybrid Forest Modeling

Doktorand*in (m/f/d) in Hybrid Forest Modeling