Prof. Dr. Carsten F. Dormann

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




Frau Eva Meier

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




Biometrie und Umweltsystemanalyse

Albert-Ludwigs-Universität Freiburg

Tennenbacher Straße 4 
79106 Freiburg i. Br.


Sie sind hier: Startseite Mitarbeiter / Staff Hannah Habenicht

Hannah Habenicht


Hannah Habenicht

Department of Biometry and Environmental System Analysis

Tennenbacher Straße 4, 79106 Freiburg, Germany
Room 03.059

email: hannah.habenicht@biom.uni-freiburg.de

phone: +49 761 203-3748
fax: +49 761 203-3751



Research Interests


- Interdisciplinary and Translational Communication

Life Sciences

Deep Learning

Scientific Machine learning

Process Modeling

Explainable AI



Curriculum Vitae




Doctoral researcher in the CRC 1597 'Small Data', Project B02. Transfer learning for forecasting short environmental time series using process-guided neural networks







MSc NeuroscienceMajoring in Neural Circuits and Behavior. University of Freiburg Germany. 
Master thesis
, Imaging Memory and Consolidation (IMaC) Lab, Department of Neuropsychology
Titled: “
Shaping memory: the effects of proactive and retroactive contextual demand on memory consolidation.

Laboratory Assistant, Eurofins GeneScan GmbH, Freiburg.








BSc, Liberal Arts and Sciences Majoring in Life ScienceUniversity of Freiburg, Germany. 

Bachelor thesis, Institute of Anatomy und Cell Biology, Department of Molecular Embryology

Titled: “Investigation into the molecular signalling pathways behind the morphological heterogeneity of reactive astrocytes at the glial scar border.

Research internship at German Aerospace Center (DLR), Department of Gravitational Biology, Cologne, Germany.

2017-2018 Year Abroad: Chinese University of Hong Kong, Hong Kong 




On-going project


Project summary:

In many fields of science, and particularly in environmental science, process models have been developed to represent our knowledge of the mechanisms underlying fluxes and states. We hypothesize that such process knowledge can substantially improve purely data-driven neural networks in small data settings. Thus, we want to expand approaches for process models in neural networks by augmenting neural networks with existing biophysical process knowledge and using explainable AI to improve process models.

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