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technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on the importance
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individuals and patients. These projects involve large-scale neuroimaging data collection at 3T and 7T, computational modeling of brain responses using machine learning methods, and cross-institutional clinical
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
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Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological
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positioning of results for partnerships, licensing, or venture creation. Your profile PhD (or equivalent experience) in Computational Biology, Bioinformatics, Systems Biology, Machine Learning, or a related
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Qualifications: Completed doctoral studies – PhD in bio-resource technology, practical implementation of Machine Learning, or a related field. Strong knowledge of Food security theory. Understanding of principles
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., MATLAB, Python) is required. Experience with machine learning is highly preferred. Ability to work independently and as part of a team. Key Requirements for PhD: Hold a Bachelor's degree with outstanding
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positioning of results for partnerships, licensing, or venture creation. Your profile PhD (or equivalent experience) in Computational Biology, Bioinformatics, Systems Biology, Machine Learning, or a related
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models