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Field
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computational approaches. You will play a hands-on role in generating and analysing high-quality data that fuels discoveries, publications and therapeutic innovation. https://www.rdm.ox.ac.uk/research
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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of good-quality data is typically limited for high-value critical assets. This PhD project will focus on developing, evaluating, and demonstrating physics-informed machine learning or domain knowledge
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the experiment design, impact analysis and icing code droplet impact solver refinement. The main impact of the work will be to provide droplet impact data and analysis at high speeds which are closer to real-time
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hardware and data handling toolchains. The research will also involve modelling, analysis and characterisation of the system and components. The PhD will include a placement of minimum 3 months with Team
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, hypothesised to drive positive impacts on macroinvertebrate diversity and sediment dynamics. Collect field data from LWS and paired control sites. Analyse through statistical analysis significant causal effects
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holistic views of complex chemical and biological systems. However, discrepancies in data structure, measurement scales, and resolution across platforms make this integration challenging. Existing approaches
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further developing both the experimental and data analysis methods that are currently used within the research team. The student will learn how to use the MMI apparatus, gaining knowledge of, for example
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involve leveraging advanced natural language processing and medical image analysis to transform imaging data into clinically relevant information. Additionally, it will explore the use of multimodal fusion
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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate