Kontakt

 

Leitung

Prof. Dr. Carsten F. Dormann

Telefon: +49 761 203-3749
Telefax: +49 761 203-3751
eMail: carsten.dormann@biom.uni-freiburg.de 

 

Sekretariat:

 

Frau Eva Meier

Telefon: +49 761 203-3749 
Telefax: +49 761 203-3751 
eMail: eva.meier@biom.uni-freiburg.de 

 

Anschrift:

 

Biometrie und Umweltsystemanalyse

Albert-Ludwigs-Universität Freiburg

Tennenbacher Straße 4 
79106 Freiburg i. Br.

 

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Marieke Wesselkamp

 

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Marieke Wesselkamp

Department of Biometry and Environmental System Analysis

Tennenbacher Straße 4, 79106 Freiburg, Germany
Room 03.063

phone: +
fax: + 49 761 203-3751  
Email: marieke.wesselkamp@biom.uni-freiburg.de

 

 

Curriculum Vitae

 2021-now             Postgraduate researcher, University of Freiburg.

2021 Research internship at the Max-Planck Institute (MPI) for Biogeochemistry, Model-Data Integration Group. Jena, Germany.
2018-2021 MSc Environmental Sciences, Profile: Environmental Modeling and GIS applications. University of Freiburg Germany. Master's thesis: Process-guided transfer learning with sparse data.
2018 Research internship at the University of York, Faculty of Biology, Department of Ecology and Evolution. York, United Kingdom.
2013 -2018 BSc Geography, University of Freiburg, Germany. Bachelor thesis: Identifying ecotypes from climate data with spatially varying coefficients.
 
 
 

 

Publications

  • Wesselkamp, M., Roberts, D.R. & Dormann, C.F. Identifying potential provenances for climate-change adaptation using spatially variable coefficient models. BMC Ecol Evo 24, 70 (2024). https://doi.org/10.1186/s12862-024-02260-z

  • Wesselkamp, M., Moser, N., Kalweit, M., Boedecker, J., & Dormann, C. F. (2022). Process-guidance improves predictive performance of neural networks for carbon turnover in ecosystems. arXiv preprint arXiv:2209.14229https://doi.org/10.48550/arXiv.2209.14229  (submitted to: Ecology letters)
  • Wesselkamp, M., Chantry, M., Pinnington, E., Choulga, M., Boussetta, S., Kalweit, M., ... & Balsamo, G. (2024). Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators. arXiv preprint arXiv:2407.16463arXiv:2407.16463v1 (submitted to: Geoscientific Model Developments)

 

 

  

 

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