Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- DAAD
- Technical University of Munich
- Leibniz
- Nature Careers
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Heidelberg University
- Max Planck Institute for Biogeochemistry, Jena
- University of Tübingen
- Humboldt-Stiftung Foundation
- Ludwig-Maximilians-Universität München •
- University of Göttingen •
- Hannover Medical School •
- Helmholtz-Zentrum Geesthacht
- Max Planck Institute for Demographic Research (MPIDR)
- University of Münster •
- University of Potsdam •
- ;
- Freie Universität Berlin •
- Friedrich Schiller University Jena •
- Helmholtz-Zentrum Dresden-Rossendorf •
- Leipzig University •
- MPINB
- Max Planck Institute for Biogeochemistry •
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Meteorology •
- Max Planck Institute for Molecular Genetics •
- Max Planck Institute for Neurobiology of Behavior - caesar, Bonn
- Max Planck Institute for Plant Breeding Research •
- Max Planck Institute for Sustainable Materials •
- Max Planck Institute for the Structure and Dynamics of Matter •
- Max Planck Institute of Molecular Plant Physiology •
- Technische Universität Berlin •
- Ulm University •
- University of Bremen •
- University of Cologne •
- University of Passau •
- University of Stuttgart •
- WIAS Berlin
- 29 more »
- « less
-
Field
-
– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
-
such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
-
with microstructural features and failure mechanisms Development of models to describe degradation mechanisms and predict component lifetime Presentation of research findings at project meetings
-
available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
-
to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
-
susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
-
of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
-
the diversity of aspartic proteases from the model plant Arabidopsis thaliana and deploy chemical synthesis, advanced modelling, protease biochemistry, mass spectrometry and structural analysis methods. A
-
these determinants, we will harness the diversity of aspartic proteases from the model plant Arabidopsis thaliana and deploy chemical synthesis, advanced modelling, protease biochemistry, mass spectrometry and
-
of future applications from the fields of structural lightweight construction, energy research and medical technology. The experimental development is closely accompanied by modelling approaches and