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Field
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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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are not limited to: network architecture design for NTN and terrestrial network (TN) convergence, intelligent traffic steering algorithms between TN and NTN, orchestration of TN/NTN resources for end-to-end
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The School of Management seeks to recruit a Research Associate to research the role of operations research in the adoption of algorithmic solution for public transport, leading to Net Zero. This
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fields of communication science such as public sphere and public opinion research, journalism and political communication through the use of innovative, computerized tools and algorithms for collecting
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advanced algorithms that align, merge, and aggregate datasets while maintaining data fidelity, the project contributes to the CAMS goal of enabling precise, accurate, and actionable analytical insights
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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consists of three major parts: sensor printing, circuit design and integration and developing of an AI algorithm and using it to teach the sensor to selectively measure desired gases . In this role, you will
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, adapt existing and develop new methodologies in robotics, design working algorithms from theories, deploy and test algorithms on hardware as appropriate, and analyse data from a variety of sources and
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administrative activities, adapt existing and develop new methodologies in robotics, design working algorithms from theories, deploy and test algorithms on hardware as appropriate, and analyse data from a variety