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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
- Deep Learning and Perturbation Models (f/m/x) 102324 Full time Neuherberg near Munich Home Office Options We are Helmholtz Munich. In a rapidly changing world, we discover breakthrough solutions
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 1 month ago
description: You will contribute to the project “Digitalization of Biomaterial Research via Deep Learning Approaches” (DC10) which involves the following tasks: Preparation of model biomaterial surfaces using
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the potential to apply these methods to different domains. Specifically, you will: Develop, implement, and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL) for scientific and large
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to different domains. Specifically, you will: Develop, implement, and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL) for scientific and large-scale datasets Implement parallel ML
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targets Development and application of deep learning (DL)‑based algorithm for 1) drug-disease link prediction (e.g., graph neural networks) and/or 2) mining of electronic health records from millions
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 19 days ago
, or a closely related field. Demonstrated expertise in developing and applying machine learning models (sklearn, etc) and deep learning frameworks (PyTorch, TensorFlow, etc.) for analysing biomedical
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 9 days ago
environment between clinic and IT also at different locations # Excellent programming skills in languages such as Python # Knowledge in interactive data visualization # Proficient use of modern deep learning
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genomic data using deep learning methods in order to identify degrading enzymes from different data resources Using Hidden Markov Models and similar tools as well as machine learning for the identification
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towards a better understanding of Earth system dynamics. In particular, we are actively involved in researching the use and development of machine and deep learning (ML/DL) approaches to model, parameterize
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innovative research on FIZ ISE research topics like ontological engineering, knowledge graphs, natural language processing, deep learning and large language models, with participation in scientific