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of research findings • Collaborate closely with internal and external researchers, including opportunities for co-supervision of students Lab Environment The T-cell Biology Group is part of the MRC WIMM
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this role, you will also provide guidance to less experienced members of the research group, including DPhil/PhD students and undergraduate project students. 3D-CAT is a new Faraday Institution research
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, “Determining extinction correlates on geological timescales”. Providing guidance to less experienced members of the research group, including research assistants, technicians, and PhD and project students
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. The postholders will support ongoing research that aims to unravel the molecular architecture of the chloroplast’s beta-barrel protein assembly machinery using structural tools. One of the posts will be focusing
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About the role We are seeking a full-time Postdoctoral Research Associate in Power System Optimisation for Second-Life Battery Storage to join the Power Systems Architecture Lab within
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full-time (part-time working will be considered minimum of 30 hours per week, 0.8 FTE). About You You will hold a PhD (or close to completion) in a relevant scientific subject though we would consider a
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of the research group, including postdocs, research assistants, technicians, PhD and project students, as well as to represent the PI and ISML when required. You will hold a Masters or PhD degree (or be close to
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and how it may respond to vaccination. You will supervise master’s and PhD students, and support the overall efforts of the lab. This post offers the opportunity to engage in cutting-edge translational
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the departments bioinformatics capabilities more broadly. You would be encouraged to take part in postgraduate teaching (mostly MSc students) that would typically amount to 2-3 lectures or tutorial per year. This
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning