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Field
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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development. The main tasks will be in photometric light curve analysis and validation (Gaia and PLATO) and data analysis and development of novel scientific algorithms and applications in the areas
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learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows for crafting
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deep learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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proofs obtained via state-of-the-art theorem provers and model-checkers. There will be a need to develop novel synthesis algorithms that can scale. There will be a need to fine-tune foundational models
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of such systems, but this is driven from the fundamental theoretical knowledge that underpin them. You will be co-creating new models, algorithms and schemes to advance the field of joint sensing and communications
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing