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Field
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, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate
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the UK Space Agency, European Space Agency (ESA), NASA and Teledyne e2v. A main focus for the CEI is the development and characterisation of detectors for space missions. One such mission is the Habitable
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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the field of energy storage for renewable power generation system. The candidate will play a central role in delivering a two-year research project focused on developing a high-efficiency, lightweight Smart
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work with researchers on the DASS programme to develop and promote freely available software, implementing new statistical methods and algorithms for identifying anomalous structure in stream settings
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position will work with researchers on the DASS programme to develop and promote freely available software, implementing new statistical methods and algorithms for identifying anomalous structure in stream
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ambitious new project on the safe deployment of medical imaging AI. The purpose of the role is to conduct world-leading research on AI for medical imaging. The goal is to develop methodologies and
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. Research Environment The project is in collaboration with two partners: (i) IDCOM at the University of Edinburgh, which develops theory, algorithms and hardware for the next generation of signal processing
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real-time, and will be tested and demonstrated on a state-of-the-art HiL rig and an autonomous test vehicle. The post is focused on the development of automotive-grade algorithms and estimators that will
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group at King's College London led by Professor Bipin Rajendran. The group at King's is part of a consortium developing multiprocessor systems-on-chip with advanced nanoscale in-memory neural processing