66 structural-engineering-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dip" uni jobs at Imperial College London in United Kingdom
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and spinouts with deep technology-based solutions for climate mitigation and adaptation. Encompassing hardware, software and hybrid solutions, the project supports climate-focused businesses across a
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achieve enduring excellence in research and education in science, engineering, medicine and business, for the benefit of society. Our strategy, Science for Humanity, is ambitious and positions Imperial as a
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– this includes Research Software Engineers (RSEs) and technical professionals working in data and computing infrastructure / High Performance Computing (HPC) roles. Led by Imperial College London, the project is
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will work within a highly interdisciplinary and international environment, collaborating closely with experts in research software engineering, technological innovation, environmental policy, and
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sits broadly within the area of engineering biology and, more specifically, the development of alternative antimicrobials, including peptide and protein-based biotherapeutics. The post is also in
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. CADI is supported by the UK Department for Science, Innovation and Technology with a remit to develop science-policy interfaces to aid the adoption of transformative AI applications across the UK
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As a Research Associate in Data-Driven Optimisation, you will work at the interface of chemical engineering, machine learning and automation, to develop next-generation workflows for the design and
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Are you an experienced PMO professional who thrives on bringing structure, clarity and strong governance to complex programmes? Do you enjoy working with senior stakeholders and enabling teams
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imaging, physiological measurement, machine learning, and high-performance computational modelling. You will work with cardiology, cardiac electrophysiology, imaging, and biomedical engineering teams
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candidates will be involved in projects exploring novel ways to incorporate topological structures into deep learning pipelines, contributing to both theoretical advancements and practical applications