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, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
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for long-term production projects including large arrays of full-recticle and wafer-size semiconductor sensors, as well as one-off R&D pieces and small commercial activities requiring a rapid turnaround
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leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis of normative frameworks and aggregation rules, and
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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
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computational social choice, and aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will
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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis
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. The scope of the research will encompass aspects such as network monitoring, routing algorithms and network-hardware benchmarking. About you You should possess a MSc/MEng in Engineering, Computer
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system with integrated sensors. You should hold or be near completion of a PhD/DPhil with relevant experience in the field of robotics, biomedical engineering, information engineering, electrical
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. This can involve IoT connected devices, physical sensors or other instruments, including non-intrusive methods and inferences from a variety of data sources. You should have some experience with experimental
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods