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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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good collaborative skills within a team. You may also have strong theoretical and programming background in AI, machine learning, mathematical modelling including physics-based models, and/or robot
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the applications of Machine Learning. Dr. Anthony Bellotti is Professor in the Department of Computer Science at UNNC. He received his PhD in machine learning from Royal Holloway, University of London
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, or computer science A PhD or equivalent professional qualification and/or experience in the field of machine learning for biology and mathematical modelling Strong planning and organising skills Excellent written and
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that you meet the following essential criteria: Have or about to obtain a PhD in computer science, engineering, mathematics or physical sciences area. Significant relevant research experience in machine
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climate change - Computer vision, e.g., colour vision, human colour vision, colour appearance models, etc. - AI technology, e.g. statistical learning, neural network learning, deep and transfer learning
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climate change - Computer vision, e.g., colour vision, human colour vision, colour appearance models, etc. - AI technology, e.g. statistical learning, neural network learning, deep and transfer learning
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similar structures from the same population. New machine learning, sensing and digital twin technologies will be developed with the aim of driving new standards for safer, greener structures in the future
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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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statistical machine learning techniques to mine self-reports and sensor data to gain new insights towards assessment and longitudinal monitoring of bipolar disorder; b) work on sleep datasets exploring