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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 5 days ago
learning/deep learning frameworks (e.g. PyTorch); experience in single-cell analysis or machine learning is advantageous. If you fulfill all the requirements, you may be eligible for a salary grade of up
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
ablation and deep brain stimulation options for pediatric patients. Special Physical/Mental Requirements Campus Security Authority Responsibilities Not Applicable. Special Instructions Quick Link https
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Classification Title: INSTRUCTOR/PKY Job Description: You belong at a K-12 teaching and learning environment that’s leading the way! If you are interested in being a part of this dedicated team
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individuals and patients. These projects involve large-scale neuroimaging data collection at 3T and 7T, computational modeling of brain responses using machine learning methods, and cross-institutional clinical
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions
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include the development of finite elements methods, as well as inverse design strategies based on deep-learning and Neural Networks approaches. The latter will then bring the project to the experimental
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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solutions that enhance ecological monitoring, improve resilience planning, and promote sustainable resource management. Development of a Detection Transformer through Attentive Deep Learning and Explainable
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools