631 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" "UNIS" Fellowship positions in United Kingdom
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- bermet.ishenbekova@ucentralasia.org
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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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-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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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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managing and developing solid-state hydrogen technology, such as developing technology for the OceanRefuel project. This wide portfolio includes single and multiple stage metal hydride compressors
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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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measurements. The Research Fellow will be embedded in a vibrant research environment, with multiple active Research Groups working on ecology, biodiversity conservation and evolution. Salary will be at Grade 6
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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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-Mobility and Clean Growth is seeking a highly-motivated Research Fellow to work as part of a team delivering an exciting, industrial collaborative research project with multiple UK partners, focused on
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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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on supporting farmers, vets and other important stakeholders to co-develop a framework for enhancing biosecurity practices on non-intensive farming systems as part of a consortia involving researchers from a