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-treatment facilities, and biorefineries. Feedstock choice, regional dynamics, and process side-streams all affect costs, energy use, and emissions. This PhD project will develop advanced computational models
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Solutions, you will apply your methods in detailed simulation studies using Imperial’s high-performance computing facilities, assessing performance in realistic scenarios. You will regularly present your
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Liverpool where, in the School of Computer Science and Informatics, we have an active group of PhD students, postdocs, and academics working at the intersection of Machine Learning, Verification and
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the goals of HEHTA, the Living Lab, and the Creative Communities programme. Co-create, build, and maintain a digital engagement platform in collaboration with stakeholders from government, healthcare
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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Deadline: 12.12.25 How to apply: https://uom.link/pgr-apply-2425 For UK students This 3.5-year PhD studentship is open to Home (UK) applicants and EU students with settled status. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject...
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Job description: A Research Assistant / Associate position, funded by a Wellcome Trust Collaborative Award in Science, is available in the research group of Prof. Dylan Childs. The grant is led by Prof. Michael Brockhurst at University of Manchester, together with lead investigators Prof Dylan...
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to apply, please use the below Programme Application Portal to get in touch with the Programme Admission Admin. Application received in other channels will be re-directed and requires apply for a second time
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emerging photonic microdevices promising to revolutionise computer, communication, and sensing technologies must be performed with unprecedented picometre (one-hundredth of the atomic size) precision
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability