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, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
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. You will then develop a predictive Machine Learning tool to support engineers in incorporating vegetated systems into design stage decision making. Finally, you will apply Life Cycle Analysis
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machine learning (ML) and artificial intelligence (AI) workflows, the project aims to create a comprehensive molecular atlas and identify novel, translational biomarkers and therapeutic targets. Project
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Error for Off-Shore Wind Measurement Systems Dr Anthony Brooms Software Engineering, Machine Learning, Natural Language Processing, Large Language Models Dr Taolue Chen Static Program analysis and
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contribute to the development of robust, reproducible machine learning workflows. The role forms part of a nationally significant research programme with strong policy relevance, working in collaboration with
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to compliance professionals for validation studies. Training spans advanced NLP (transformer fine-tuning), financial crime typologies, privacy-preserving machine learning, and product-oriented development. You
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills