653 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation"-"U.S" Fellowship positions in United Kingdom
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including malaria, have experience and understanding in using multiple metrics for analysis, experience with design and implementation of study protocols, experience of data analysis in Stata and programming
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the TWICEASGOOD project, attending team meetings, disseminating results Contributing to the development research ideas related to project themes of gender, political communication and “everyday sexism” Analysing
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will be based in the Environmental Lab at the University of Southampton, with opportunities to collaborate with leading researchers across multiple institutions within EBIC. The research will be
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part of the research team to develop software platforms for supporting multi-user AR/VR applications. Key Responsibilities Participate in the research project with Principal Investigator (PI), Co-PI and
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desirable. The successful candidate will be based in the Environmental Lab at the University of Southampton, with opportunities to collaborate with leading researchers across multiple institutions within EBIC
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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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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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sales, IP generated) and/or public understanding of the discipline or similar Main Duties The responsibilities may include some but not all of the responsibilities outlined below. Develop research
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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