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development of data architectures, analytics tools, and interoperability frameworks aimed at integrating building energy data into flexibility markets. They will work closely with an established team
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data-driven net zero carbon energy system. The appointee will support the development of data architectures, analytics tools, and interoperability frameworks aimed at integrating building energy data
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of this project. The candidate’s background can be either in in traditional music analysis (with a PhD in musicology, music analysis or music theory) and/or in computational musicology, digital humanities
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recovered, transcribed, and edited within the scope of this project. The candidate’s background can be either in in traditional music analysis (with a PhD in musicology, music analysis or music theory) and/or
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collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be expected to lead the research into innovative ways in which the machine
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. The successful candidate will have the responsibility of developing, in collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be
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expertise in energy systems, data analysis, and performance evaluation. They will contribute to the integration of hardware and software components from a research perspective, ensuring high-quality data
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assist with the technical research agenda, contributing expertise in energy systems, data analysis, and performance evaluation. They will contribute to the integration of hardware and software components