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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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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Description of work You will be working in the laboratory of Marta Bally (https://ballylab.com
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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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, epidemiological data and outcome modelling using AI-assisted algorithms, as well as multi-modal data integrations. An established data infrastructure with expertise and computational pipelines for these analyses
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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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administrative and technical services—all within a setting that offers attractive employment conditions. To learn more about the department, please visit: https://www.umu.se/en/department-of-computing-science
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Group, https://www.chalmers.se/en/departments/e2/research/communications-antennas-and-optical-networks/communication-systems/ , employing around 45 people, including 8 faculty and 25 PhD students. During
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum