Sort by
Refine Your Search
-
Listed
-
Employer
- DAAD
- Forschungszentrum Jülich
- Leibniz
- Technical University of Munich
- Nature Careers
- Humboldt-Stiftung Foundation
- RWTH Aachen University
- GFZ Helmholtz Centre for Geosciences
- Max Planck Institute for Demographic Research (MPIDR)
- University of Göttingen •
- University of Münster •
- University of Potsdam •
- Biomedical Center, LMU Munich
- GFZ Helmholtz-Zentrum für Geoforschung
- Hannover Medical School •
- Helmholtz Zentrum Hereon
- Helmholtz-Zentrum Geesthacht
- Justus Liebig University
- Leibniz-Institute for Plant Genetics and Crop Plant Research
- MPINB
- Max Planck Institute for Biogeochemistry •
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Molecular Genetics •
- Max Planck Institute for Plant Breeding Research •
- Max Planck Institute for Sustainable Materials •
- Max Planck Institute of Molecular Plant Physiology •
- Technische Universitaet Darmstadt
- Technische Universität München
- University of Cologne •
- University of Stuttgart •
- 21 more »
- « less
-
Field
-
Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 15 days ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using
-
. You will employ the trypanosome model established in our group to study its swimming behavior in soft tissue-like surroundings. This project is a part of the DFG-SPP 2332 priority program “Physics
-
system can reduce dependence on fossil fuels – but at the same time brings new challenges and uncertainties. Disruptive events and changing political, social, and technological conditions can have a
-
management platform that connects institutes to facilitate a rapid and efficient exchange among experimental and computational groups Devising an approach in invertible predictive modeling that links
-
play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
-
for analysis of the data you create. Your findings will be a vital part of understanding the evolution of cancer drug resistance in cell models and in patients. About you: You’re excited about our research
-
://github.com/FZJ-IEK3-VSA/RESKit ). This framework currently uses historical weather data to model energy output and will be further developed to allow the simulation of renewable electricity production under
-
interdisciplinarity and transfer of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and
-
Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
-
plant genetic mechanisms that coordinate mycorrhizal interactions with plant P and water status, root system development, and soil microbial communities. Using maize and rice as models, we will: 1