644 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"UNIS" Fellowship positions in United Kingdom
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deepfake technology. As part of this team, we are seeking either a Research Fellow to lead; or a Senior Research Associate to contribute to leading: the development of a forensically assured verification
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& Medicine at King’s College London. Reporting 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
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To be an active member of the research team assisting in the development and pre-clinical and industrial translation of novel drug delivery systems. To assist in the planning and delivery
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(coordinated by Developing Healthy Communities NI and Alcohol Forum Ireland). They will work at local level to ensure close collaboration with SUMIT activity leads/partners and relevant stakeholders in
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the Spatial Biology Facility, you will lead the development and optimisation of high-resolution spatial biology and multi-omics data analysis pipelines. Your primary focus (80% of your time) will be on leading
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sufficient evidence to develop a novel antimicrobial treatment for GIM, which could prevent the development of GAC. Role Summary Work within specified research grants and projects and contribute to writing
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International travel may be required for this role. Background This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological prediction. The Research Fellow will develop
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international specialists. Within Cumulus, you will lead the development of “downscaling” methods for sub-seasonal (2-4 week) forecasts. Our priority will be to implement deep-learning based methods, to turn
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deepfake technology. As part of this team, we are seeking either a Research Fellow to lead; or a Senior Research Associate to contribute to leading: the development of a forensically assured verification
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African teams and other international specialists. Within Cumulus, you will lead the development of “downscaling” methods for sub-seasonal (2-4 week) forecasts. Our priority will be to implement deep