774 web-programmer-developer-"University-of-Illinois---Chicago" Fellowship positions in United Kingdom
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COG-MHEAR is a world-leading cross-disciplinary research programme funded under the EPSRC Transformative Healthcare Technologies 2050 Call. The programme aims to develop truly personalized
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molecular biology and possess sufficient specialist knowledge in the discipline to develop research programmes and methodologies. The successful applicant will also be able to work collaboratively, supervise
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against different interfering systems. Develop, with colleagues, a spectrum sharing database for use by the JOINER community and design and develop models using AI/ML and other techniques to ensure
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to the Director of the HAB and working in close partnership with the Spatial Biology Facility, you will lead the development and optimisation of high-resolution spatial biology and multi-omics data analysis
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academia or industry to contribute to the development of research programmes and methodologies Grade 8 Qualifications Honours Degree and PhD in an appropriate discipline such as Earth observation, data
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investigates how the unique capabilities of quantum computing can be leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand
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online. You will be expected to plan and conduct work using approaches or methodologies and techniques outlined in the project design and will be responsible for writing up your work for publication. You
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a particular emphasis on neuroscience, mental health, and cancer. We are creating a vibrant community of researchers across both academic and biotech sectors engaged in major research programmes which
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research programme at King’s, focussed on establishing a competitive research niche and positioning them to apply for intermediate-level post-doctoral fellowships to consolidate their careers, from Research
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leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models—expressivity