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application! We invite applications for a fully funded PhD student position to join the research group of Andrew Winters to work on challenging problems in Computational Mathematics for accurate and reliable
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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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. The PhD programme includes course work amounting to 75 ECTS as well as PhD thesis work. Your qualifications The holder of the position must meet the requirements for both general and specific eligibility
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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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studies. In connection with your admission to the doctoral program, your employment as a PhD student is handled. More information about the doctoral studies at each faculty is available at Doctoral studies
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, Ethnicity and Society (REMESO), is looking for 2 or more PhD candidates. The candidate will work on an independently outlined project that can be aligned to one or several of the five REMESO research streams
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studies. In connection with your admission to the doctoral program, your employment as a PhD student is handled. More information about the doctoral studies at each faculty is available at Doctoral studies
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), offers an exciting opportunity to work at the forefront of AI security, tackling some of the most pressing challenges in the field. As a PhD student, you devote most of your time to doctoral studies and
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application! We are looking for a PhD student in Statistics and Machine Learning Your work assignments We are looking for a PhD candidate to work in the intersection of computational statistics and machine
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning