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hemocompatible coating strategies to improve membrane–blood interactions. - Model and optimize membrane performance using computational tools, machine learning, and artificial intelligence Work Plan - Synthesis
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applying machine learning and computational methods for protein design, in close integration with experimental enzymology and biocatalysis. The tasks include: Development and application of AI and machine
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on storytelling approaches that integrate scientific research with community perspectives/knowledges and that explore different modes of public engagement with ocean biodiversity loss, particularly in relation
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 14 hours ago
genome-based influenza and/or SARS-CoV-2 risk assessment algorithms, designing broadly protective influenza and SARS-CoV-2 vaccines using Machine Learning and Artificial Intelligence, modeling the impacts
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for diagnosis and prognosis of different sarcomas. He/she will develop and train deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research
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Description When the future of their child is threatened, many parents increase the protection of their child, aiming to shield them from looming menace. Yet some parents respond differently. These parents not
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. We expect successful applicants to work towards improving the collaborations and connections among the different areas. We invite applicants to visit our website to learn more about current research
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning for batteries, with the support of
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county across the state, Texas A&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about
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the research activities entrusted to the officer take place: This ANR project lies at the interface between statistical learning (mainly deep learning) and combinatorial optimization (mainly stochastic and