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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
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similar field; expertise in programming skills and statistical data analyses, including machine learning; affinity with environmental exposure modelling and high-performance computing; strong reporting and
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interest in environmental health and Exposome research; expertise in programming and quantitative data analysis, including machine learning in R/Python; affinity with bioinformatics; strong collaboration
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-resolution, open-access climate projection ensembles with statistical and machine learning-based resampling techniques (e.g., k-nearest neighbours) to simulate weather-dependent energy supply and demand
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& machine learning
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biodiversity or occurrence data (e.g., GBIF). Understanding of species distribution modelling or trait-based ecology. Interest or experience in applying AI or machine learning methods to ecological questions
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university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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This forward-looking PhD project merges performance science with advanced data analytics and machine learning to further enhance performance prediction in elite rugby union. The successful candidate
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: university and, if applicable, PhD degree (e.g. Master/Diploma) in mathematics, physics, materials science or related subjects basic knowledge of computer programming (e.g. Python, Matlab and C++) excellent