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to work on meaningful projects with direct clinical relevance. About the role In this role, you will develop and implement computer vision and deep learning algorithms to analyse CT and MRI data from
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researcher to join the Turing AI World-Leading Fellowship research programme led by Professor Alison Noble. This exciting and ambitious research aims to develop new AI for shared human-AI decision-making in
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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for plausible narratives of regional climate change, novel algorithms for rare event sampling or ensemble boosting, and the development and use of hybrid climate models combining physics-based and ML components
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includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals that are closely related to applications and applied research
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the following objectives: 1. Characterize 3-D Urban Structure and Change: Utilize data from multiple remote-sensing platforms and deep learning algorithms to generate high-resolution maps of 3-D urban structure
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related to patterns of political violence, terrorism, counterterrorism and their effect on one another as specified in the TERGAP project; Develop (build, train, and test) algorithms to machine-code event
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learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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will be on developing new mathematical and algorithmic tools that advance our understanding of these areas and their connections. Possible research directions include explicit constructions of error