23 machine-learning-"https:" "https:" "https:" Postdoctoral positions in United Kingdom
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, Organometallic, Organic Chemistry and Machine Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating
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the laboratory of Prof Piotr Dudek at the University of Manchester. The role is directly associated with the EPSRC-funded project on "On-sensor Computer Vision," carried out in collaboration with Imperial College
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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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mixing. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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testing of deuterium permeation experiments in support of the UKAEA’s LIBRTI project (https://ccfe.ukaea.uk/programmes/fusion-futures/librti/). This will include expanding capabilities on UoM’s Gas-Driven
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projects. Our recent work has focused on the theory and applications of quantum walk algorithms, variational quantum algorithms and their optimization, adiabatic quantum computation, and quantum machine
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watermarks. Publishing research in leading journals and conferences in speech, audio, and machine learning, and contributing to open-source releases of software, trained models, and reproducible research
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. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You will experience
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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. Desirable experience in AI-assisted image analysis, machine learning, computational modelling, or building predictive models from biological imaging or cell–material interaction datasets. Strong experience in