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Field
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ambitions We are seeking a candidate with: Educational background PhD in Architecture, Landscape Architecture, Computational Design, Computer Science, Urbanism, or a closely related field. Specialization in
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computer codes to solve some of the daily research problems and have experience with high performance computing. You should have a PhD in Chemistry, Physics or Materials Science with a proven research track
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We are seeking a full-time Postdoctoral Research Assistant in Physics-Based Modelling of Advanced Batteries to join the Department of Engineering Science under the supervision of Prof Charles Monroe
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We are seeking a full-time Postdoctoral Research Assistant in Physics-Based Modelling of Advanced Batteries to join the Department of Engineering Science under the supervision of Prof Charles Monroe
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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to a cross-faculty Master of Data Science programme. We were ranked 1st for Criminology in the Times Good University Guide 2024. The Role Applications are invited for a Postdoctoral Research Associate to
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. Qualification requirements The selected candidate should have a master’s degree in a related field: e.g., civil engineering , mechanical engineering , computational materials science , or applied mathematics
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at the School of Engineering & Materials Science, Queen Mary University of London, working closely with researchers at the Digital Environment Research Institute & Barts Heart Centre. Barts Heart Centre provides
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: Essential: Experience of working in a laboratory Some initial experience in basic techniques of molecular biology e.g. PCR, cloning, or qPCR Desirable: Previous work with genetically modified materials
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Overview 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