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research assistant in applied adversarial machine learning. This exciting opportunity will contribute to the D-XPERT (AI-Based Recommender System for Smart Energy Saving) project. The D-XPERT project
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academic forums. The successful candidate will contribute novel machine learning approaches advancing audio-visual AI to transform video of real-world scenes to object-based representation and neural
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simulations of Li/Na dendrite growth and mechanical stress-strain behaviour of materials in solid-state batteries, and to develop machine learning methods to assist multiscale modelling for zero-excess solid
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will be to develop generative machine learning models and signal processing algorithms for sound generation, given prompts from text and/or video. This work is built on the recent contributions of CVSSP
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an outstanding Research Software Developer for the development of a mobile acoustic sensing system suitable for environmental acoustic sensing. The post holder will work in close collaboration with leading machine
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management, which can involve schema modelling, formatting, annotation, enrichment, search and discovery. Experience in Machine Learning model development/optimisation and workflows for time-series (tabular
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cosmological merger trees, with machine learning tools to calculate the gravitational wave background based on black hole and galaxy properties. The successful applicant will assess the efficiency of massive
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are encouraged to apply. Prior experience working with large datasets, machine learning techniques and/or high-resolution simulations of dwarf galaxies would also be advantageous. How to apply Applicants should