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Background in probabilistic methods Experience with the application of AI algorithms and probabilistic methods Good programming skills Personal characteristics To complete a doctoral degree (PhD), it is
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of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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detection and classification algorithms using measured and/or simulated data, such as current pulses from cable faults (breakdown), partial discharges and external noise. In addition to being part of
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of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning
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, the candidate must have experience working with various biological specimens, including either viruses or bacteria. The candidate is expected to have training in cell handling and in working with genetically
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: To establish technologies for the measurement and modulation of nuclear NAD levels and turnover using pharmacological and genetic approaches. This will include LC-MS quantification To develop and
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a data infrastructure to study the connection between environment, genes, and mental health. It will gather and organize large collections of data on people's health, environment, and genetic
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high-Arctic Svalbard and other Arctic regions, the wintercress (Barbarea vulgaris) is an established alien species. Hypotheses for its success include multiple introductions from different genetic
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University’s residency requirements. Galice Guillaume Hoarau via Unsplash Galice Guillaume Hoarau Qualification requirements MSc degree or equivalent in a relevant discipline such as genetics, epigenetics