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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Materials Discovery Using Synchrotron Radiation, Machine Learning, and Artifical Intelligence
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quantum attacks Verifiable and privacy-preserving machine learning (e.g. proving model integrity or fairness without revealing training data) Scalable ZK-proof constructions for federated and
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and artificial intelligence/machine learning may be applied are encouraged to apply. If you are interested in research in brain-machine interfaces, and are unsure about whether you have the right
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is expected between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects
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Machine Learning Engineer – Ship Design & Hydrodynamics (KTP Associate) Employer UNIVERSITY OF SOUTHAMPTON Location Southampton, United Kingdom / Hybrid Salary £41,064 to £45,064 Closing date 26 Oct
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Machine Learning Engineer – Ship Design & Hydrodynamics (KTP Associate) Location: London (hybrid working may be available) A Computational Ship Hydrodynamics and Design Optimisation specialist is
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existing research skillset in novel machine learning approaches, we are recruiting a team of outstanding researchers in cloud compute architecture design and event-triggered computing to work with us on
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based within the School's research environment, working with an academic project team on robotics, machine learning and cyber-security. The project is supported by the University's research and
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying computational