Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- DAAD
- Forschungszentrum Jülich
- Technical University of Munich
- Leibniz
- Humboldt-Stiftung Foundation
- Fraunhofer-Gesellschaft
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- GFZ Helmholtz Centre for Geosciences
- Nature Careers
- CISPA Helmholtz Center for Information Security
- Constructor University Bremen gGmbH
- GFZ Helmholtz-Zentrum für Geoforschung
- Hannover Medical School •
- Heidelberg University
- Helmholtz-Zentrum Geesthacht
- Helmholtz-Zentrum München
- Heraeus Covantics
- Leipzig University •
- Ludwig-Maximilians-Universität München •
- Max Planck Institute for Human Cognitive and Brain Sciences •
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig
- Max Planck Institute for Meteorology •
- Max Planck Institute for Sustainable Materials GmbH, Düsseldorf
- Max Planck Institute for Sustainable Materials •
- RWTH Aachen University
- Technische Universität Berlin •
- University of Münster •
- University of Tübingen
- 18 more »
- « less
-
Field
-
[maps] and the TUM Garching campus [maps], and all members are affiliated with both institutes. As a PhD candidate in our group, you will drive your own research on machine learning methods in close
-
Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
-
of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
-
for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
-
corrosion properties; ii) determine, using sensitivity analysis, impact of the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine
-
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
-
-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
-
mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods
-
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
-
machine learning We offer: Academic freedom to pursue your scientific interests related to infection biology, inflammation, gene expression, and intracellular organization Competitive salary including