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and robust multidisciplinary program focused on the management of patients with complex head and neck cancers. In addition to clinical duties, the successful candidate will teach surgical residents
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Responsible ML Explainable Machine Learning techniques will be explored to identify important clinical predictors and improve the transparency of model predictions. The research will consider model robustness
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. The Risk and Infrastructure Systems Lab, led by Asst. Prof. Alex Sixie Cao, develops quantitative methods for understanding and managing the reliability, robustness, and safety of structures and
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technologies that make production and infrastructure systems more efficient, robust, flexible, and sustainable, thereby strengthening Europe’s competitiveness and technological sovereignty. As part of our
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-world recordings of speech, breathing and cough. You will explore self-supervised learning, Transformer-based architectures, masked prediction and contrastive learning, with a strong focus on robustness
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are available. Expected Contribution The research aims to develop a robust, interpretable and generalisable AI framework for cardiovascular risk prediction and clinical decision support. The study will consider
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silylated peptides and the Reches group in peptide self-assembly and biomaterials. It would lead to novel robust peptide-based biomaterials that can provide cues to mammalian cells and prevent bacterial
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to publications in top-tier international conferences and journals, as well as real-world implementations. The role includes designing novel algorithms, building robust software systems, and collaborating with
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recordings of speech, breathing and cough, developing self-supervised learning and foundation-model approaches that are robust to noise, demographic variation and different recording environments. You will
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environmental data into practical insights and robust computational solutions.