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leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models—expressivity
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models, techniques and methods. Person Specification A first degree in Chemistry, Physics, Chemical Engineering, Materials Science or related subject, and a PhD (or close to completion) in an area relevant
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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have: A PhD in Statistics and relevant work experience in developing risk prediction models for Alzheimer’s Disease and dementia through secondary analysis of biobanks and cohort studies. Evidence of
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of fungal pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology
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Fixed Term Contract for 24 months We welcome applications from passionate and skilled post-PhD or experienced researchers in malacology, epidemiology, schistosomiasis modelling, or spatial data
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Assurance and verification offerings for AI are fragmented and target specific areas of AI lifecycle (e.g. model assurance or prompt injections) without offering a unified view of the security
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model studies, we aim to gain mechanistic insights into the disease pathologies and ultimately develop treatment strategies. Mission Statement Michigan Medicine improves the health of patients
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, modeling cancer risk, risk mitigation, early detection, prevention, and cancer care delivery. The primary project will be a part of the Clonal Hematopoiesis Chemotherapy and Radiation Effects (CH CARE Study
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of sampling techniques. Proficiency in statistical methods and techniques, including developing and validating quantitative models, hypothesis testing, regression analysis, and multivariate analysis and the