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trajectories? Passionate about archival research and oral history? Self-motivated and ready to learn new research skills? The Department of History is looking for two PhD candidates to undertake archival and
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in advanced methods for the analysis and classification of EEG and auditory signals. The group of the project is multidisciplinary, with experts in signal processing, machine learning, acoustics and
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that leverages the full spectrum of available data sources. The thesis should address the following questions: 1) How can one improve perception systems using data coming from different sources? 2) How
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: Collaborate with other PhD candidates and researchers working on the project to share insights and learn from its different sub-projects. The successful PhD candidate will be based at the Faculty of Industrial
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Join us to explore the mechanics of soft matter through a unique blend of theory, hands-on experiments, and machine learning. Job description Soft matter such as polymers and hydrogels
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, disability studies, (co-)design methods, and Human-Computer Interaction (HCI). The ideal candidate is passionate about creating socially impactful inclusive co-design methods and eager to collaborate directly
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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northern Europe. Our research covers a broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in
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, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the intersection of energy systems and markets, privacy and cybersecurity, forecasting, optimization, control, game
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monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in