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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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media and internet infrastructure computing cultures and materialities as heritage values and economies in algorithmic/data cultures social and cultural perspectives on dismantling communication networks
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. The applicant should furthermore
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successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment