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foundations of medical deep learning. The project focuses on novel self-supervised objectives, information geometry, mitigating representation bias for rare pathological findings, and building next-generation
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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FieldComputer scienceEducation LevelMaster Degree or equivalent Skills/Qualifications Strong foundations in Machine Learning and Deep Learning Excellent Python programming skills Experience with PyTorch
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environment. Successful and rapid development and deployment of the technology will ensure EU's leadership in the exploration and exploitation of deep space, the next commercial space frontier. The program is
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strong interest in foundational research in the above-mentioned research areas strong programming skills, preferably in Python, including experience with deep learning frameworks (e.g., PyTorch, TensorFlow
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or complementing traditional physics-based approaches by data-driven ones, using Machine-Learning (ML). Such approaches allow enormous gains of time, in a way that can be related to the astonishing efficiency
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and experience with modern deep learning frameworks (e.g. PyTorch) Solid background in machine learning, ideally with experience in NLP, large language models, or sequence modeling Interest in clinical
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 3 months ago
immunology with deep focus on T cells and Treg biology Hands-on experience with in vivo mouse models (FELASA certification is a strong plus) Practical experience in flow cytometry (FACS), including protocol
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German Cancer Research Center in the Helmholtz Association (DKFZ) | Oettingen in Bayern, Bayern | Germany | 3 months ago
. Strand-resolved mutagenicity of DNA damage and repair. 2024. Nature 630: 744. https://doi.org/10.1038/s41586-024-07490-1 Nicholson MD, Anderson CJ, et al. DNA lesion bypass and the stochastic dynamics
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of Science and Freie Universität Berlin, Humboldt-Universität zu Berlin, and Technische Universität Berlin. The IMPRS-KIR traces the deep entanglements of knowledge and its resources from a long-term and