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of students and can include, technique development, microscopy-spectroscopy, analysis/programming (including AI and machine learning) and materials-focused studies. We use innovative high-resolutionidentical
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have: A PhD (or equivalent) in a relevant discipline (e.g., biostatistics, machine learning, computer science, clinical informatics, natural language processing). Strong skills in data analysis and
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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mobile health technologies enable the continuous capture of rich, multimodal physiological and behavioural data. These data when analysed with Artificial Intelligence (AI) and machine learning methods can
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optimisation. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model
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worked in MRI research previously or have strong computational / AI / machine learning skills used in other areas of research. Essential criteria PhD qualified in relevant subject area Ability to work as
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Computer Engineering, Computer Science, Computational Science-based discipline or equivalent experience The preferred candidates will be flexible and independent to learn new languages and find the right
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will be spent on both aspects of the role. You should have a postgraduate degree in Statistics, machine learning or a related discipline and a track record of methodological research relevant to Prob_AI
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, Applied Machine Learning, Neural Networks and Deep Learning as well as Machine Learning for AI and Data Science and Bayesian Theory and Data Analysis. We are looking for an associate able collectively
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, aiming to enable faster, cheaper, and more robust production through machine learning, multi-scale modelling, and advanced process simulation. The successful candidate will be based at the Bristol