500 web-developer-"https:"-"https:"-"https:"-"Institut-Agro-Rennes-Angers" Fellowship positions in United Kingdom
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Applications are invited for a Research Associate/Fellow position to work on a project involving the use of additive manufacturing to develop a release system for fungal biopesticides for outdoor
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We are seeking a computational glycobiologist to join a multidisciplinary project developing next-generation enzymatic tools for antibody glycoengineering. The role will focus on applying state
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disciplines? Do you have relevant research experience in chemistry or biochemistry and a keen interest in developing molecular tools to probe complex protein/glycan interactions? Are you eager to develop your
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Resilinet Battery Room Design for Next-Gen Electric Habour. The project is in collaboration with two local industry collaborators. Under this job title, you will develop a CFD modelling platform to evaluate
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research building on previous research from the Silvi research group. The research will focus on the development of novel reactions based on photoredox-radical chemistry. The new radical processes will be
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to develop your own research project within the programme with opportunities to focus on either analysis of MRI imaging, cerebrovascular physiology, or other effects of treatment, depending on prior skills and
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the UK) and work within the Department of Cancer and Genomic Sciences. Their main role will be to apply and develop new methods for precise genome manipulation to create transgenic reporter models
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of human embryonic development, especially regarding the emergence of the body plan. Research in the Developmental Models laboratory aims to uncover the principles of human development using cutting-edge in
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Studies. Working under the supervision of Professor Rita Abrahamsen (Chair of African Studies), the successful candidate will develop and pursue an ambitious and original programme of research within
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during