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on empowering Sustainable Energy Communities to actively participate in local energy markets and provide demand-side flexibility within a data-driven net zero carbon energy system. The appointee will support the
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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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Communities to participate in local energy markets and provide demand-side flexibility within a data-driven, net-zero carbon energy system. The Research Assistant will work closely with post-doctoral
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ADFLEX Project, which focuses on enabling Sustainable Energy Communities to participate in local energy markets and provide demand-side flexibility within a data-driven, net-zero carbon energy system
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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