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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back
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Datasphere4Trust project focusing on the design, development and deployment of a distributed and adaptive policy framework to enhance trust and security across federated data space networks. This project is part of
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to have experience in one or more of the following areas, such as: wireless networks/communications, machine learning/vision, intelligent transportation systems, intelligent sensing/localization, or signal
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/communications, machine learning/vision, intelligent transportation systems, intelligent sensing/localization, or signal processing. In line with our Athena SWAN ambitions we especially encourage women to apply
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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safety, pharmacoeconomic and cost-effectiveness outcomes, budget impact, organisational and social considerations, and ethical and legal issues. The findings will help inform clinical, policy and
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candidates will have experience in artificial intelligence, deep learning or decision support applied to RF sensing, wireless communications and signal processing. A strong background in multimodal data
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learning or decision support applied to RF sensing, wireless communications and signal processing. A strong background in multimodal data analysis is expected, complemented by strong capacity for teamwork
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partners across EU and an end-user from USA came together to delivers a safety-first digital twin fusing physics-based traffic dynamics with AI to prevent safety-critical events before crashes occur. While