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advances research on the changing material conditions of media, technology, culture and heritage, and how they intersect with environmental, institutional, industrial, and social conditions. Research in
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, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy
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scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on
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here. The project will be carried out in a collaboration between STIMA (main supervisor: Prof Fredrik Lindsten) and the Centre for Environmental and Climate Science, Lund University (co-supervisor: Prof
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. For more information about STIMA, read here. The project will be carried out in a collaboration between STIMA (main supervisor: Prof Fredrik Lindsten) and the Centre for Environmental and Climate Science