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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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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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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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using jets and heavy-flavor probes. The candidate will also participate in algorithm and software development for the LHCb trigger system as part of the Real Time Analysis Project. A Ph.D. in experimental
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project team on “Participatory Algorithmic Justice: A multi-sited ethnography to advance algorithmic justice through participatory design” (PARTIALJUSTICE) to examine issues of justice and participation in
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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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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
adaptation, speciation, and biological innovations. This project aims to redefine our understanding of gene loss alongside gene gain in plant evolution, focusing on developing novel genomic approaches
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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