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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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of applying geometric approaches and techniques to solve problems. Networking, actively engaging with and valuing other research areas. Published papers/preprints in relevant academic journals. Excellent oral
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-hazard risk and recovery framework and open-source tools to quantify and manage combined earthquake and flood impacts on urban buildings, transport networks and community recovery. The Research Fellow will
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the capacity to coordinate complex, distributed projects with clear communication and a supportive approach. Essential Skills: ● Demonstrable experience with single-cell RNA-seq analysis, including QC
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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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of applying geometric approaches and techniques to solve problems. Networking, actively engaging with and valuing other research areas. Published papers/preprints in relevant academic journals. Excellent oral
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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
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Full-time: 35 hours per week Fixed-term: 13 months The opportunity: Conduct original research on neuronal networks underlying the processing of natural visual stimuli, using in vivo large-scale two
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complex mental health needs. About the role Working at the intersection of research and real-world implementation, you will lead integrated knowledge-mobilisation activities, co-produce evidence with
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the Infocomm Technology cluster at SIT. This project addresses the critical challenge of delivering real-time Artificial Intelligence (AI) services in Internet of Things (IoT) networks, where latency and