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ecology and associated fisheries. A gap in local knowledge/data and technical capacity to coordinate and deliver usable data tools was identified. This gap inhibits the dynamic understanding of the impact
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and spatial resolution of regional ocean model data around Europe, having broad use cases in mind such as offshore energy and aquaculture. As global climate variability intensifies, precise regional
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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monitoring allow personalised diagnosis and treatment. ENDOTRAIN will train a new generation of interdisciplinary experts who merge clinical endocrinology, AI, data science, engineering, ethics and law into
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of interdisciplinary experts who merge clinical endocrinology, AI, data science, engineering, ethics and law into an integrated field of digital endocrinology. The programme focuses on adrenal
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cloud data centres. However, this can come at the cost of performance and slower function processing, which manifests as higher energy consumption and increased operational costs (OpEx) for operators. So
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. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a leading technical university globally
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and partner urban sites, the successful candidate will collect and analyse real-world environmental data to evaluate how vegetation interacts with the built environment. You will be supported by a
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of these materials. Implementation of artificial intelligence (AI) and machine learning (ML) to establish the connection between the existing models and material data (both literature and the baseline established in
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Scandinavian language is a requirement. Strong analytical skills and relevant methodological knowledge. For detailed information about what the application must include, see the section "Application’" Qualified