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PhD Scholarship in Big Data and Analytics for Crop Genetics Modern agriculture and biomedical research are driven by the availability of big data and the development of data science. The recent
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algorithms to achieve real-time control and process optimisation of drainage pipe networks. It seeks to improve the operational efficiency of urban wastewater systems, reducing energy consumption and extending
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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algorithm that allows accurate simulation of fluid transport processes in porous media coupled with chemical reactions (e.g. dissolution and precipitation). The algorithm will be validated firstly against
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algorithms for resource-efficient hydroponics and evidence-based frameworks for integrating green space exposure for improved student wellbeing. This project will potentially enhance urban food resilience
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energy resources. The expected outcomes include technical advancement of distributed algorithms for managing energy resources at customer premises. The benefits include more resilient, secure, private, and
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therapeutic targets. The scholarship provides opportunities to apply statistical genetics approaches to dementia research within a collaborative and supportive research environment. Student type Future Students
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Desirable skills Experience with experimental design and behavioural data collection Familiarity with generative or agentic AI systems, explainability (XAI), or algorithmic fairness Skills in statistical
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image diagnosis. The expected outcomes are development of a software prototype, technical advancement in medical image diagnosis and the creation of novel AI algorithms. Potential project benefits