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Field
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A PhD (or near completion) in relevant discipline such as Finance; Economics; Business Analytics; Sustainability or related discipline. Subject Matter Expertise Strong knowledge of sustainable Finance
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that the microbiome may play in providing stress resilience. Applicants must be highly motivated, with a PhD in plant biology or a related area. PhD students about to submit their thesis in a relevant area will be
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be close to completion of, a PhD/DPhil in a relevant scientific field (e.g., Immunology, Biology, Biochemistry, Medical Laboratory Science), sufficient specialist knowledge and relevant experience
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on research progress and outcomes, communicate complex information, orally, in writing and electronically and prepare proposals and applications to external bodies possess a relevant PhD (or nearing completion
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-quality academic articles and publish them in internationally recognised, reputable journals. You will mentor and co-supervise PhD students affiliated with the project. You will assist with project-related
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, circulation) is encouraged. You are prepared to teach a methodology course or workshop on contemporary history and media. We expect: Completed doctoral/PhD thesis in history or another cultural or social
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probing experiments Data analysis Publications in peer-reviewed journals and presentations at international conferences Supervision of trainees and students This is part of your profile: A PhD degree in
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(approximately 1 course per semester for MA and PhD students within the research interest of the post holder), Conduct innovative and impactful research that pushes the boundaries of the field, collaborating with
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and experience: Essential criteria PhD in bioinformatics, computational biology, or a related discipline * Extensive experience and expertise in analysing/ training models on biological or chemical
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for the Research Associate, Grade 7 level, position must have a PhD in a quantitative biology discipline, statistics or machine learning along with a proven track record of research using statistical modelling