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evolution of a recently discovered family of clonally transmissible cancers which affect several species of marine bivalves. This new position is part of an ERC-funded project examining genome evolution in
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an account with our recruitment system (if you have not already) and apply online. Applicants must have (or be close to obtaining) a PhD. Appointment at Research Associate level is dependent on having a PhD
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will have excellent written and oral communication skills and a demonstrated ability to work effectively as part of a team. Candidates should have a PhD in Iranian Linguistics or Iranian Studies, with a
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to develop and improve current simulation methods (quantum and classical) to study such systems. The overarching aim of the post-doctoral position is to develop and apply computational approaches that enable
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will have a PhD in a relevant area (or be close to completion) and a good understanding of neurotransmission and behaviour. We welcome applicants with experience in some of the following areas and with a
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stakeholders who benefit from these ecosystems. The successful candidate will have a PhD in a relevant subject such as interdisciplinary ocean science, or ecology and preferably a good understanding of the
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. They will use this knowledge to develop strategies to engineer expression in plants, seeking to optimise yields from metabolic pathways. Key skills The successful candidate must have a PhD in molecular or
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. degree in a relevant discipline and prior mouse work and/or laboratory experience. This position is offered at Grade 5 on the university scale. Interviews are expected to take place in late August
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of working effectively under pressure, with excellent communication skills and a strong focus on teamwork and accurate data recording. This position offers the opportunity to contribute directly to impactful
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to Stephen Burgess, commencing in January 2023. Successful candidates will be appointed to a 1-year fixed-term position at the Research Assistant (Grade 5) level. This position is for full-time hours (37 hours