423 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" research jobs at Nature Careers
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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contribution of the PhD will be the derivation of multilayered approaches for motion planning and control based on the XS-Graphs, where both model-based and learning-based solutions are foreseen. This includes
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climate change, fostering sustainable agriculture, and preserving global biodiversity ( https://www.psb.ugent.be/ ). In this respect, an important research topic of the center is the interaction between
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diseases, Genome Biology, 2024 S. Hudaiberdiev et al., Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits, PNAS, 2023 S. Li et
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Description VIB.AI, the VIB Center for AI & Computational Biology, is a young research center dedicated to combining machine learning with in-depth knowledge of biological processes. Our mission is
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related to the exchange of momentum, energy, and mass. Significant challenges persist in understanding processes and feedbacks. The Land-Atmosphere Feedback Initiative (LAFI, https://lafi-dfg.de ) is an
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understanding and generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research
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image/signal processing, particularly in computer vision.Strong programming skills and experience with at least one deep learning framework e.g. TensorFlow or PyTorchFamiliarity with machine learning and
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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fellow to join our translational research program in macrophage biology/immunology. Our team takes a systems approach—integrating multi-omics, network science, machine learning, and comprehensive in vitro