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The Open Bioeconomy Lab, headed by Dr Jenny Molloy (openbioeconomy.org), is seeking a Research Associate for "EngZyme: Engineered Enzymatic Catalysts for In-flow CO2 Upcycling", an EPSRC Prosperity
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
on integrated GRMs. The successful candidate will hold a PhD (or be close to obtaining) in Applied Physics, Material Science, Nanotechnology, Chemistry or Electrical Engineering with experience in photonics
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. It is essential that they are knowledgeable in current behaviour change science and techniques and specifically have experience of designing topic guides and administering in-depth, semi-structured
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will play an active role in advancing and evolving this technology to expand its capabilities, with a primary focus on developing ligands and tool compounds targeting therapeutically relevant proteins
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-subsaturated air (but where no contrail was formed). Research Associate: Hold a PhD in Aerospace Engineering, Atmospheric Science, Physics, or a closely related discipline*, or equivalent research, industrial
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, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination at the point of commencing this post
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The role The post is part of the Wellcome Discovery Award research programme “Collective Action for Race Equity in Health and Social Care”, which aims to address and dismantle structural factors
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and is a leading multidisciplinary research-intensive technology university delivering economic, social and cultural benefits. For more information please visit: https://www.brunel.ac.uk/about/our
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networking with colleagues and students; planning and organising research resources and workshops. Successful applicants will have or be near to completing a PhD in computer science, information engineering
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading