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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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on site/WFH options and flexible start/finish times, and genuine career progression opportunities via the academic promotions process. About You Completed PhD or equivalent in Design or equivalent
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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, complex omics datasets (e.g. transcriptomic, genomic, proteomic), with demonstrated skills in statistical modelling; experience in machine or deep learning is advantageous. Emerging track record of research
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variable models (e.g., CLIP, GLIP, MaskCLIP). Knowledge of Transferability in Machine Learning is desirable. Knowledge in Active Learning is desirable. Programming skills and experience with dataset
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using AI and machine learning in developing new tools for better management of TIC members transformer fleets. Guided by experienced academic staff and supported by Industry experts through the TIC
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collaboratively with colleagues from multidisciplinary disciplines Excellent time management and planning skills, with a commitment to delivery Strong background in machine learning and/or deep learning, and signal
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including the application of artificial intelligence and machine learning. You will engage with industry, government, and research collaborators, fostering partnerships that deliver outcomes aligned with
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of the centre/team/facility Build new and/or improve existing research-focused engineering technologies, systems, prototypes, machines and/or processes Perform other duties as reasonably directed by the Research