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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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mortality using traditional and new forms of data, with a focus on developing and low-income countries. The successful applicant will spend 18 months at LSHTM and enrol in the PhD programme, with fees funded
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interdisciplinary research environment is highly desirable but not necessary as training will be provided. Experience of computer modelling is desirable but not essential as full training will be provided in
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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on Data Science for Construction Productivity. The researcher's responsibilities will include
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integration of genomics, proteomics, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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used to measure motion and deformation. It provides comprehensive full-field deformation data, essential for analysing complex materials, structures and model validation. The DIC community has developed
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The overall aim of this PhD project is to understand how indoor air quality (IAQ) affects the health of children and adolescents, including their mental health with the aim of creating healthier
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structural molecules. Unravelling the ways in which these motifs are encoded into GAGs by their biosynthetic machinery is the fundamental challenge behind the BBSRC-funded GlycoWeb project. This 4-year PhD