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modelling framework to predict key thermal hydraulic parameters for boiling flows within complex geometries at high heat flux conditions, relevant to the engineering design of thermal management elements
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the Centres for Doctoral Training (CDTs) in Net Zero Technologies, Sustainable Composites Engineering, and Digital Metal. Based within the Faculty of Engineering, you will act as the first point of contact
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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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to communicate with clarity on complex information. Excellent communication skills in Chinese is a plus. Ability to build relationships and collaborate with others, both internally and externally. About Us Join a
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experience in problem solving. You have excellent written and verbal communication skills to communicate a complex information with clarity. What you’ll get in return: 27 days annual leave (pro rata for part
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and embracing a complex, intersectional identity in this community. You effectively influence, step up to challenge the status quo, and develop equitable, inclusive networks. You will have experience
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have experience coordinating complex projects, supporting committees, and managing events such as workshops and networking sessions. A proactive team player, they are comfortable handling multiple tasks
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have worked within a large, multi-technology network environment, supporting both on-premise and cloud based services. You will also have experience of monitoring systems to manage performance and
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, to inform and enhance drug optimisation. Employ machine learning to analyse complex datasets, extract meaningful insights, and guide the optimisation of drug molecules. Contribute to interdisciplinary