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developed within the School of Clinical Dentistry. Simulated data produced in Computer Science will augment EIS data collected from these constructs. These machine learning models will be developed from both
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are looking for an ambitious candidate with a strong background in mathematical and statistical methods for both physics-based modelling and machine learning, and their application to engineering problems in
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, multiplex imaging analysis) Essential Application/Interview Experience in bioinformatics or computational analysis of biological data (e.g., transcriptomics, spatial omics, image analysis, machine learning
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theoretical physics, quantum computing, or machine learning and have completed or be in the final stages of a PhD in this or a related discipline. Main duties and responsibilities Design and analyse quantum
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will primarily support the Head of School (Professor George Panoutsos, Chair in Computational Intelligence) and his research activities in the area of Machine Learning (ML) for Engineering, focusing
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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greener transportation and energy. Building on recent advances, the successful candidate will use a powerful combination of dynamical systems theory, optimisation, DNS and machine learning to model and
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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experience of treatment. The overarching aim of the project is to use machine learning methods to understand why many people who are referred for treatment will drop out prematurely. To do this, two studies