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within physically sensible design spaces avoiding the need to learn every pathological flow scenario and making machine learning both efficient and reliable. The ultimate goal is to retain the fidelity and
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machine learning model to rule out heart attacks in the emergency room, which has the potential to translate to large savings for healthcare systems in the world, (2) a computational modelling to assist in
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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part-time positions are available for those committed to application-oriented education. Job Responsibilities Teach and assess at least 3 courses in the following core subjects: Data Analytics & Machine
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) for engineered microbial strains. Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways. Integrate multi-omics datasets into computational workflows
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candidate will: Develop genome-scale metabolic models (GEMs) for engineered microbial strains. Design and implement machine learning algorithms to predict strain performance and optimise metabolic pathways
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background knowledge in statistical genetics, machine learning, omics data analysis, and computational biology. Additional knowledge in transcriptomics, statistics, and foundation models is also desired. Fixed
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Safe and Inclusive At NTU, we continue to build an inclusive culture that encourages, supports and celebrates the diverse voices and experiences of our students and colleagues. By championing positive wellbeing, we promote an environment where all can thrive and reach their full potential. We...
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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specialised expertise in the Machine Learning for Engineering sub-theme. Candidates from all areas in machine learning are encouraged to apply, with a special focus on the areas of (i) information theory and