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Role Description A research position is available to support research to enhance observations of human exposure to air pollution and other pollutants using sensor-based approaches. Air pollution is
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artificial intelligence methodologies. The successful candidate will work at the forefront of computational biology, developing novel approaches for large-scale genomic data analysis and contributing
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of Engineering at the University of Nottingham. The position is part of a larger project in collaboration with the School of Chemistry and you will work in a highly collaborative and interdisciplinary team
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. • Support supervision and mentoring of PhD and MSc students. • Engage in knowledge exchange, contributing to grant writing and collaborative partnerships both internally and externally. You should have
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life cycle analysis methodologies. Proficiency with modelling and simulation software relevant to TEA and LCA. High analytical ability to analyse and illuminate data, interpret reports, and bring new
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Magnini in the Faculty of Engineering at the University of Nottingham. The position is part of a larger project in collaboration with the School of Chemistry and you will work in a highly collaborative and
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interpretation. This role will involve working closely with civil engineers, computational modellers, Physicists, and geophysicists to translate raw sensor outputs into actionable insights. Role Summary Lead data
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students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration
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artificial intelligence methodologies. The successful candidate will work at the forefront of computational biology, developing novel approaches for large-scale genomic data analysis and contributing
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challenge due to limited patient data—especially at the single-cell level—making traditional modelling approaches difficult. This project tackles that challenge by integrating multi-omics and clinical data