Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- Technical University of Munich
- Leibniz
- Forschungszentrum Jülich
- Nature Careers
- University of Greifswald
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- Constructor University Bremen gGmbH
- Helmholtz Zentrum Hereon
- Christian-Albrechts-Universitaet zu Kiel
- Existential Consciousness Research Institute Schloss Kränzlin
- Fraunhofer-Gesellschaft
- GESIS - Leibniz Institut für Sozialwissenschaften
- Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt
- Helmholtz-Zentrum Hereon
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich
- Max Planck Institute for the History of Science •
- NEC Laboratories Europe GmbH
- University of Potsdam, Faculty of Science
- University of Tübingen
- cellumation GmbH
- 10 more »
- « less
-
Field
-
employment. Starting date: 09.04.2026 Job description:PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 987 - 2026/WP 1 Work location: Hamburg Application deadline
-
) and related to the major multi-national initiative “GOE-DEEP” supported by the International Continental Scientific Drilling Program (ICDP) and aiming to study the climate at the time of the first rise
-
EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 2026
-
machine learning and deep learning methods will be developed and applied within interdisciplinary research networks. The professorship should also contribute to the generation and supervision
-
related field Solid understanding of machine learning, especially deep learning and transformer models Practical experience with Python and ML frameworks (e.g., PyTorch, HuggingFace, NumPy, sklearn) Basic
-
Training Group Information (JTT) https://arxiv.org/pdf/2107.09044 [3] Hacohen, Weinshall (2019). On the Power of Curriculum Learning in Training Deep Networks http://proceedings.mlr.press/v97/hacohen19a
-
following areas: Strong foundation in machine learning, optimization, and deep learning algorithms, including Transformer architectures. Hands-on experience or solid theoretical knowledge of LLMs/SLMs
-
(PyTorch, scientific Python) with solid experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image
-
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 3 months ago
deep learning (x/f/d/m) Background With the project Deepcloud, we will leverage the machine-learning revolution to understand clouds and their role in the climate system. We aim to train a deep learning
-
host chromatin pathways (DFG Research Unit DEEP-DV, FOR5200). The group uses experimental infection systems, an array of high-throughput sequencing methods, and single-molecule live-cell imaging