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individually, make a real difference. The role To meet its legally binding 2050 net zero target, the UK must retrofit 29 million homes to eliminate greenhouse gas emissions. While such retrofits can improve
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systems, with emphasis on criteria for robustness, reliability, and stability. Topics of interest include the study of symmetries and equivariance and their implementation in deep neural networks
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, the fellowship must run from 1st October 2025 to 31st March 2026.. You can use the Fellowship to: undertake significant new research; complete key publications resulting from your PhD; develop research networks
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systems, with emphasis on criteria for robustness, reliability, and stability. Topics of interest include the study of symmetries and equivariance and their implementation in deep neural networks
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that you meet the following essential criteria: Hold or be about to obtain* a PhD in Molecular biology, Biochemistry, genomics, or a related discipline. (*PhD to be completed within 4 months of the closing
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for recent PhD graduates to establish independent research careers. These 6-month fellowships (October 2025 - March 2026) offer a crucial bridge for promising researchers to develop their careers while
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to research proposals and writing bids for research grants Write peer reviewed academic publications of the highest standards of significance and impact Supervise less experienced researchers and PhD students
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(or equivalent) or a Masters degree in a social science or other relevant area Have or be about to obtain a PhD in a relevant subject. Experience: Specific, relevant research experience working on a project that
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Action (BISCA) , and other strategic research initiatives such as the Met Office Academic Partnership and Birmingham’s Turing University Network . The recently renovated Elm House, is a building fully
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. Experience in commercial CFD package such as ANSYS package is preferred. Candidate preferably should be familiar and have sufficient experience in using ANSYS FLUENT, Meshing tool and post-processing software