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Field
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is to be the “EO innovation hub” connecting EO with a growing ecosystem of disruptive and transformative innovations such as AI, machine learning, quantum computing, edge computing, metamaterials and
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, electrical and computer engineering, data science, informatics, biomedical engineering, or a related field. Preferred: Demonstrated expertise in AI-driven drug discovery, machine and deep learning
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
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-validation by droplet digital and real-time PCR. Contribute to the host–microbiome analyses (GWAS/mGWAS, metagenomics) and the integrative modeling led by the graduate student. Apply machine-learning and AI
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. ● Demonstrated expertise in causal inference, with interest in methods development. ● Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning
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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
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
inventories) with satellite remote sensing data (e.g., spaceborne lidar and/or hyperspectral observations) and apply machine learning and deep learning approaches to address these questions. This position is
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | 2 months ago
on LazySlide ( et al Nature Methods ), our scalable software foundation, and our deep learning framework for age prediction (Abila et al., Nature Medicine, in press) to engineer a body-scale machine learning