Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- DAAD
- Nature Careers
- Leibniz
- Forschungszentrum Jülich
- Technical University of Munich
- University of Bonn •
- Hannover Medical School •
- Helmholtz-Zentrum Geesthacht
- Ludwig-Maximilians-Universität München •
- Max Planck Institute for Biogeochemistry, Jena
- Technische Universität München
- University of Tübingen
- University of Tübingen •
- 3 more »
- « less
-
Field
-
Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
-
contributes to the improvement of climate prediction models. The Atmospheric Chemistry and Atmospheric Microphysics departments are looking for a committed doctoral student to carry out this project. You can
-
attractive that the city is predicted to have the highest population growth in NRW. With over 4,800 international students from more than 135 countries, the university contributes significantly
-
) Leipzig and Leipzig University Hospital. One of the aims of PollenNet is to predict pollen levels in the air, using observations of flowering plants collected via the Flora Incognita app. Your tasks First
-
. D. positions funded by the ERC (European Research Council) to work on the 'EFT-XYZ' (Effective Field Theories to understand and predict the Nature of the XYZ Exotic Hadrons) project-advanced-ERC-2023
-
, you will develop highly accurate computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function
-
computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably
-
to describe ocean turbulent fluxes #developing theoretical and conceptual models to understand and predict ocean mixing #work as an integrative part of a motivated multidisciplinary team within the institute
-
highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing
-
prediction of queue dissolution by combining traffic flow theory with data from roadway and AMOD sensors, nonlinear optimization of the signal plan, cooperative control of traffic signals and AMOD vehicle