Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Forschungszentrum Jülich
- Technical University of Munich
- DAAD
- Leibniz
- Nature Careers
- Fraunhofer-Gesellschaft
- GFZ Helmholtz-Zentrum für Geoforschung
- University of Oldenburg
- Brandenburgische Technische Universität Cottbus
- CISPA Helmholtz Center for Information Security
- Heidelberg University
- Helmholtz-Zentrum Hereon
- University of Regensburg
- Academic Europe
- Deutsches Elektronen-Synchrotron DESY
- European Magnetism Association EMA
- Free University of Berlin
- Hannover Medical School •
- Heinrich-Heine-Universität Düsseldorf
- Helmholtz Zentrum Hereon
- Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Helmholtz-Zentrum Geesthacht
- Helmholtz-Zentrum für Infektionsforschung GmbH
- Karlsruher Institut für Technologie (KIT)
- LEUPHANA UNIVERSITY OF LUENEBURG
- Lehrstuhl für Nachhaltige Thermoprozesstechnik und Institut für Industrieofenbau und Wärmetechnik
- Leibniz-Institut für Präventionsforschung und Epidemiologie – BIPS GmbH
- Ludwig-Maximilians-Universität München •
- Max Planck Institute for Biogeochemistry, Jena
- Max Planck Institute for Demographic Research (MPIDR)
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Meteorology •
- Max Planck Institute for Sustainable Materials •
- Technische Universität Berlin •
- University Hospital Jena
- University Hospital of Schleswig Holstein
- University of Greifswald
- University of Münster •
- 29 more »
- « less
-
Field
-
the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
-
models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
-
Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
-
degree in computer science (or a related field) Rich experience in devising machine learning models, methods, and algorithms for computer vision and image processing. Scientific track record with
-
this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
-
benchmarking of deep learning sequence-to-sequence architectures Implementation of new machine-learning layers and model components Application of tools for genome analysis and molecular evolution The position
-
, machine learning, energy technology or related subjects Prior experience in building predictive models using regression techniques, neural networks (CNN, GNN) or symbolic regression Experience in
-
machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
-
-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
-
microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments