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-leading software testing research, this project will develop techniques to automatically discover scenarios in which the robots may fail – in advance of their deployment – ultimately making them more
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to support a project that aims to create openly available computational tools for automated imaging analysis of neurodevelopmental disorders. Genetic testing has transformed our understanding
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sustainability assessment framework and computational tool using Model-Based Systems Engineering (MBSE) and lifecycle modelling techniques. Apply and test the framework through case studies (e.g., ADR, ISRU, In
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
developed a dataset by conducting high-velocity impact experiments on CFRP specimens using controlled testing setups. The multimodal dataset is to be processed using X-ray CT scans, SEM imaging, and
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into the potential of pharmacogenomic testing to deliver patient benefit in patients with mental health conditions. The student will evaluate how pharmacogenomic testing can aid prescribing and deprescribing decisions
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We are seeking a talented and motivated Postdoctoral Research Associate (PDRA) to join the Energy and Power Research Group for a 12-month appointment to develop, implement and test a numerical model
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acceptable and meaningful (Work Packages 1 & 2). The core of the research (Work Package 3) involves a longitudinal study to collect smartphone data and test the validity of the co-developed AI. The data
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-power embedded electronic system within the capsule. The assembled capsule will be tested in a simulated GI environment. The University is uniquely positioned to benefit any applicant interested in a
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farms at relatively close proximity can be relevant when considering their annual energy production. This project will examine the uncertainty of various types of numerical models, from fast-computing
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance