48 evolution "https:" "https:" "https:" "https:" "https:" "https:" "BioData" research jobs at University of London
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://www.seresearch.qmul.ac.uk/cefg/ ). The Arguello lab (http://arguellolab.org/) focuses on understanding the genetic and cellular bases of sensory evolution – What are the rapid changes that underlie differences in the way
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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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aspiration pneumonia, hospital acquired infections, and reduced nutritional intake. About the School/Department/Institute/Project The critically ill patients represent a vulnerable group. The development
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assisting with co-design workshops and interviews with children and parents. Working closely with a research team across several collaborating institutions, the post holder will contribute to the development
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collection and analysis, the development of academic publications as well as engagement and dissemination activities. We seek a passionate, effective and collegial person to join our interdisciplinary team
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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 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