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Field
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly
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fundamental and demand-driven research on modelling and analysis of complex social networks. This line of research is broadly focused on modelling, analysis and estimation of complex network models in
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of complex systems, networks, and large-scale data Machine learning, generative AI, NLP, or algorithmic decision systems Ideal applicants will have a strong background in operations research, statistics
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, methodology, and delivery to drive high-quality, impactful outcomes. You will operate as a subject matter expert within a supportive research environment, providing informed advice, resolving complex issues
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synthetic ones. The Genomics group develops AL/ML methods to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease. Current areas of interest
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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battery fire combustion and spread. Strong skills in turbulence modelling, CFD mesh generation in complex 3D geometries. Proficient in handling large data sets and the ability to analysis and interpret
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contribute to both pre-clinical and clinical studies—including major clinical trials and the Peripheral Vascular Biobank. You will lead complex research activities, support ethics and governance submissions
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Medical - Clinical Internal Number: A-175743-11 General Description Our fellowship offers a unique occupational therapy approach to address the complex needs of individuals with acquired brain injuries (ABI
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in securing research funding is essential, as is demonstrable expertise in complex modelling techniques such as machine learning, network neuroscience, or related computational approaches. You will