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of machine learning and advanced molecular dynamics techniques for molecular simulations and to study Nucleic acids structures and their interactions. For more information, please visit https://nyuad.nyu.edu
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-computational loop, using analysis results to guide the design of the next round of automated experiments (design of experiments, active learning). Ensure reproducibility and FAIR data practices: version
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provable guarantees AI-based cybersecurity: applying learning and AI-assisted techniques to network security, e.g., automata learning from security logs, validation of protocol models, and verified defensive
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this, the postdoctoral researcher will combine machine learning, molecular dynamics simulations and high performance computing (Isambard AI). Applicants must have a PhD in an appropriate area of computational chemistry or
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Associate, Information Science Posting Number req27167 Department Information Science Department Website Link https://infosci.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights The
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between Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; multiple courses on topics such as leadership for academic staff; multiple courses on topics such as time
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 15 days ago
identity, physical abilities, religion or age. In case of equal qualifications, preference will be given to applicants with severe disabilities. Learn more about diversity at Helmholtz Munich Our commitment
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of creating robust radio frequency fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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gliomas. The project integrates generative AI, interpretable models, uncertainty estimation, and federated learning to improve clinical decision-making, data privacy, and personalized medicine