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for Multi-Agent Decision-Making, https://oceanerc.com ). This timely project will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making
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highly motivated and technically proficient Research Fellow in Control and Embedded Systems Engineering to support the development and deployment of intelligent, safety-enhanced BMS technologies within a
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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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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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progression once in post to £48,149 Grade: 7 Full Time, Fixed Term contract up to March 2028 Closing date: 13th August 2025 Background This research project aims to establish the theoretical and algorithmic
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highly motivated and technically proficient Research Fellow in Control and Embedded Systems Engineering to support the development and deployment of intelligent, safety-enhanced BMS technologies within a
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. This role will involve developing and applying analysis plans using a variety of advanced methods with the support of project supervisors. The postholder will have completed a PhD in a relevant discipline and
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river system Develop, test and apply algorithms for the processing and analysis of satellite data drawing on the latest physics-based and/or data-driven techniques Contribute to work on the automation and
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web technologies Experience in teaching bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing