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Research Project“ Transforming Cardiac Research: Visual Exploration and AI Prediction Modeling of Real-Life, Multi-Modal Data” as a PhD-Position in machine learning. You will work alongside leading experts
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on an exciting Wellcome-Trust funded project. The research focuses on decoding neural representations across dynamic brain states through advanced computational analysis of large-scale neural recordings. What you
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Research: Visual Exploration and AI Prediction Modeling of Real-Life, Multi-Modal Data” as a PhD-Position in machine learning. You will work alongside leading experts at the Computational Imaging Research
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visual and auditory cortices using techniques such as cross-modal decoding, unit reliability analysis, and shared variance component analysis (SVCA) Create comprehensive data visualisations and perform
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revealing factors in multi-speaker and multi-modal data and find ways to detect and hide them using natural language processing and audio analysis. We look at anonymisation, but also go beyond to study
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tackles fundamental challenges in multimodal representation learning by developing novel approaches to align distinct embedding spaces from speech and sign language modalities. Sign languages encode
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of infrastructure networks, which remain fragmented and hinder the coordination needed to address cross-sectoral implications of demand changes (e.g., modal shifts, the use of home batteries). At the same time
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, you will: Design and perform fluorogenic and nano-photonic DyeCycling experiments. Write/adapt analysis code to process fluorescence trajectories and extract kinetic information. Evaluate bioconjugation
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understanding of protein systems. As a PhD student, you will: Design and perform fluorogenic and nano-photonic DyeCycling experiments. Write/adapt analysis code to process fluorescence trajectories and extract
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and respirometry, in healthy controls and patients with ME/CFS. You will collect, analyse and curate these multi-modal datasets. Your will write several scientific publications about the collected data