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Imperial College London. I graduated in Applied Physics from Caltech, and have held academic positions at the University of Bath, Imperial College London, and the University of Munich. In March 2022 I
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University of Cambridge. The position includes extended visits to Cambridge's Department of Applied Mathematics and Theoretical Physics. You will also have regular opportunities to work with the
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theoretical and computational high-energy physics. The research contributes to the world-leading PYTHIA Monte Carlo Event Generator , which serves as the baseline for the majority of experimental
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light, focusing on controlled light-matter interactions at the nanoscale. Driven by the fascinating optical physics and photonic applications across various fields, my work spans many topical areas in
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-effective image reconstruction. This project explores cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models to tackle challenges in MRI
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decade of AI-powered engineering. This is a collaborative research project with Atlassian . Check out some recent work: - https://www.atlassian.com/software/rovo-dev - https://www.atlassian.com/blog
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decade of AI-powered engineering. This is a collaborative research project with Atlassian . Check out some recent work: - https://www.atlassian.com/software/rovo-dev - https://www.atlassian.com/blog
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-cell RNA-seq datasets; (2) cross-validating the pattern in independent datasets and (3) associating observed spatial patterns with known literature in congenital heart disease. References https
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-Ouellette, S., & Rudzicz, F. (2021). BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data. Frontiers in human neuroscience . [2] https
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-Ouellette, S., & Rudzicz, F. (2021). BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data. Frontiers in human neuroscience . [2] https