46 evolution "https:" "https:" "https:" "https:" "https:" "https:" "University of Cambridge" research jobs at University of London
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About the Role Postdoctoral Research Assistant to join Dr Diu Nguyen’s group (https://www.diunguyenlab.org/). We study the biological mechanisms underpinning leukaemia development and progression
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East London. REACH is delivered across three East London boroughs and brings together a large network of community partners alongside academic collaborators from the University of Cambridge. The Research
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at the Brain Repair Centre, University of Cambridge. About You Applicants should have a PhD in neuroscience, cell biology, stem cell biology, or a closely related discipline, with ability to work
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. The goals are to develop a greater understanding of basic mechanisms of immunological protection versus pathology, and to apply this knowledge to the development of interventions and the identification
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on the development, validation and use of organ-chip models. We are particularly focused on the development and use of joint-on-a-chip models including synovium, bone, cartilage, fat along with inflammatory components
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efficient running of laboratory operations to support both ongoing projects and the development of early-career researchers. About the School/Department/Institute/Project Located at the Charterhouse Square
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responsibility for the development of the simulations but will contribute to all aspects of the project in collaboration with the PI and a PhD student to be funded by the grant. About You The successful candidate
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About the Role We are seeking a postdoctoral research assistant to join the group of Dr Mirjana Efremova in the Centre for Cancer Evolution, to investigate epigenetic regulators driving therapy
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About the Role You will contribute to the construction and quality control of the ATLAS Inner Tracker (ITk). The ITk group at Queen Mary University of London has played a key role in the development
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the design, development, deployment and evaluation of NeoShield’s applied machine-learning systems, the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis and the real-time ward