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, optimization techniques, and climate change scenario analysis. The successful candidate will contribute to the development of intelligent models and decision-support tools that enhance the performance
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will involve a combination of longitudinal, descriptive, and cross-sectional studies to better understand the bidirectional influence of training and match-demands of tier 3 football during
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. Development of multimodal AI models that fuse data from multiple types of sensors to accurately model and predict wind turbine blade damage. Establish and develop data science pipelines for wind turbine blade
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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will work with a comprehensive dataset spanning multiple seasons of elite competition, featuring team KPIs, individual player and ball GPS tracking data, and player wellness information. The project
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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications
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and/or Python. Experience in, and aptitude for, complex statistical modelling (inc. mixed effects regression models and/or Bayesian statistics). Excellent written and spoken English. Desirable (traits
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, individualized education. Faculty are committed to creating a student-centered learning community that embraces multiple viewpoints. It is a department in which learning, research, and professional activity inform