566 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" positions at University of Sheffield
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BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. A long-term attachment (6-12 months) and
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see this link for further information: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language. Please see this link for information on how to apply: https://sheffield.ac.uk/postgraduate/phd
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System (IELTS) average of 6.5 or above with at least 6.0 in each component, or equivalent. Please see this link for further information: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language
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. This PhD project will build directly on this work by using ideas from machine learning—originally developed to study the movement of larger organisms—to understand how bacteria process information in
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the third Higgs boson decays to two tau leptons, or another highly sensitive combination. The student will gain expertise in machine learning techniques for signal-background discrimination and will
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with the CDT’s aim to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be
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acids institute (https://sheffield.ac.uk/nucleic-acids) and the centre for Single Molecule biology (https://smash.sites.sheffield.ac.uk/), providing additional expertise. This project will contribute
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neurological and cardiovascular disorders. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying References GONZÁLEZ-SANTANA, A., ESTÉVEZ-HERRERA, J., SEWARD
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intelligent sensing, followed by detection of the important events.In the light of autonomous decision making, the project aims at developing machine learning algorithms for knowledge extraction from data
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of technologies that are used in academia, industry and many related careers. Visit http://www.sheffield.ac.uk/sgs to learn more. Please apply for this project using this link: https://www.sheffield.ac.uk