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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep
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admissions and mortality records; Develop physics-guided and graph-based models for high-resolution environmental exposure estimation; Build explainable AI pipelines to identify which exposures matter most
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research, including data collection and data analysis; Affinity with the lived experiences of young people and communication skills tailored to working with youth; A strong awareness of current topics and
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research by: 🐟 Developing a smad3 knockout zebrafish model, 🔬 Optimizing in vivo visualization of aortic enlargement and molecular dysfunction, 💊 Developing a pathogenic signalling-based drug screening
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approach, combining machine-learning–enhanced text-as-data analysis with qualitative discourse analysis. The project aims to produce a set of high-quality scholarly outputs, including peer-reviewed journal
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morphology to directly link gene-regulatory cell states to functional neuronal phenotypes. This ambitious project integrates wet-lab experimentation with advanced computational analysis, and is ideal for a
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estimation, and the detection and compensation of sensor drift and degradation. The candidate will develop data processing and modelling approaches combining signal processing, statistical analysis, and data
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morphology to directly link gene-regulatory cell states to functional neuronal phenotypes. This ambitious project integrates wet-lab experimentation with advanced computational analysis, and is ideal for a
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young versus older adults using EEG. Analyses will focus on Event-Related Potentials, time-frequency analysis and functional connectivity measures within a cognitive-motor dual task paradigm. To enhance
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PhD project investigates how children distribute visual attention across verbal and non-verbal communicative signals during storytelling. You will examine: children’s visual attention during