Sort by
Refine Your Search
-
Listed
-
Employer
- DAAD
- Forschungszentrum Jülich
- Technical University of Munich
- Nature Careers
- University of Tübingen
- Hannover Medical School •
- Leibniz
- Ludwig-Maximilians-Universität München •
- University of Göttingen •
- Brandenburg University of Technology Cottbus-Senftenberg •
- Giessen University
- Helmholtz-Zentrum Hereon
- Technische Universität Berlin •
- University of Münster •
- University of Regensburg
- Universität Tübingen
- Deutsches Elektronen-Synchrotron DESY
- Deutsches Elektronen-Synchrotron DESY •
- GFZ Helmholtz-Zentrum für Geoforschung
- Helmholtz Zentrum Hereon
- Helmholtz-Zentrum Geesthacht
- Hertie School •
- Karlsruher Institut für Technologie (KIT)
- Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH
- Max Planck Institute for Biogeochemistry, Jena
- Max Planck Institute for Biological Intelligence •
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Meteorology •
- Max Planck Institute for Sustainable Materials •
- Max Planck Institute for the Study of Societies •
- RPTU University of Kaiserslautern-Landau •
- Saarland University •
- Technische Universität Dresden
- University Hospital Jena
- University Hospital of Schleswig Holstein
- University of Bamberg •
- University of Potsdam •
- Universität Hamburg •
- 28 more »
- « less
-
Field
-
machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
-
, prototyping, programming (device communication, databases) Experience in the following areas is also a bonus: electrocatalysis, rheology, coating technology, machine learning Intrinsic motivation to show
-
and machine learning methods. Knowledge of constraint-based metabolic modelling will be considered a strong advantage. The ideal candidate is highly motivated, capable of working both independently and
-
seismicity in the area at unprecedented resolution. Leveraging and improving state-of-the-art machine learning techniques, template matching and other techniques, you will derive a high precision catalogue of
-
elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning assisted
-
applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under various operating conditions and develop strategies
-
using X-ray and neutron scattering. The main research areas are materials for photovoltaics, proteins in solutions and at the interfaces, complex nano-structured materials and machine learning tools
-
modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as
-
Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
-
machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute