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– NSF CBET project on sustainable computer networks, with a focus on carbon emissions reduction and network telemetry. You will contribute to the development of a framework that reduces the carbon
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MaRes Hub and the Horizon Europe project SEASTARS, both of which aim to advance maritime sustainability through the integration of innovative emission-reduction and energy-efficiency technologies across
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to build and maintain reproducible analysis pipelines. Working knowledge of machine learning methods relevant to neural data, including dimensionality reduction (eg, PCA, UMAP, NNMF), manifold learning, and
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document the data reduction and photometry pipelines required for high-precision time-series photometry. You help coordinate the observatory’s participation in international follow-up programs (e.g
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through the integration of innovative emission-reduction and energy-efficiency technologies across multiple ship classes and shore power applications. The project seeks to deliver industry-relevant