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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
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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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