51 evolution "https:" "https:" "https:" "https:" "https:" "https:" "BioData" research jobs at University of London
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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
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will encompass bio-informatician analysis, organ on-a chip development, small molecules testing, precision medicine trials, and development of a single cell platform. About Queen Mary At Queen Mary
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our team and help in the development of a new class of precision therapies that remodel the tumour extracellular matrix (ECM) to convert immune-excluded tumours into therapy-responsive ones. These ECM
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development opportunities. In addition, we offer a range of work life balance and family friendly, inclusive employment policies, flexible working arrangements, and campus facilities. Queen Mary’s commitment
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pension scheme, 30 days’ leave per annum (pro-rata for part-time/fixed-term), a season ticket loan scheme and access to a comprehensive range of personal and professional development opportunities. In
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of Aarhus is a partner), and contribute to open-source tools that will benefit the wider software development community. You will be expected to conduct independent research, publish in leading academic
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Professor Dario Farina, in the Department of Bioengineering at Imperial College London. The focus of the research will be on the development of soft actuators, from material and structural perspectives, with
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development opportunities. In addition, we offer a range of work life balance and family friendly, inclusive employment policies, flexible working arrangements, and campus facilities. Queen Mary’s commitment
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will develop the mathematical/computational tool-box to describe the evolution of ecDNA and test predictions of these theories in existing and newly generated cancer genomic data. The position will be