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or physiology. Further, postdocs should have a deep interest in scientific collaboration between researchers using theoretical and empirical approaches. The five projects are: A. Sensing the breaking point
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, deep learning, natural language processing, and generative AI models. • Collect, clean, annotate, structure, and analyze datasets required for AI model development and evaluation, including text data
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infrastructure, mobility demand, and power grid operations. On top of this environment, a deep-learning-based learning will be developed to enable decentralized and coordinated decisions on EV user charging
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Infrastructure? No Offer Description We offer: ● a unique opportunity to participate in a research project that utilises state-of-the-art data and methods (i.e. online tracking data and deep machine learning
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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to areas such as deep learning, natural language processing, computer vision, reinforcement learning, ethical/trustworthy AI, and interdisciplinary applications of AI/ML in various domains. We welcome
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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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populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk
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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have