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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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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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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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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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research in the field of AI for healthcare autonomous systems. Activities on non-healthcare systems could occasionally be requested. • Undertake research from algorithm development to real time
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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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of classification algorithms Correlate/Integrate In Vivo and Ex Vivo metabolite analysis to understand the key metabolic pathways in different tumour types and subtypes Identify and harmonise MRI and MRS acquisition
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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
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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