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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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decision-making for an optimal energy system, with specific focus on cost-effectiveness, emission reduction, and social acceptability. D2ET will develop a comprehensive digital platform for planning energy
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decision-making for an optimal energy system, with specific focus on cost-effectiveness, emission reduction, and social acceptability. D2ET will develop a comprehensive digital platform for planning energy
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in correctly identifying high acuity cases within an increasing volume of lower acuity calls. This project will use mixed methods to assess the impact of this change. Data modelling and analysis will
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on the analysis of Partial Differential Equations. The position covers four years of third-cycle studies, including participation in research and third-cycle courses. The last day to apply is May 30, 2025. Project
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barriers, the societal needs, storm surge barrier and ecological impacts. Key research questions will be: How can adaptation decisions on storm surge barriers in the Dutch delta be linked to other decisions
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to develop a decision support framework. This research will blend literature analysis, theoretical study, modelling and empirical analysis, contributing valuable insights into the world of farmers’ decision
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, applying advanced modeling techniques and analyzing large-scale datasets to contribute to healthcare policy decisions. The PhD project will be supervised by Dr. Nora Franzen and Professor Maarten J. IJzerman
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computational skills (using R, modelling software, working on a remote linux-based server) and experience in analyzing Next Generation Sequencing data, including PCA, outlier analysis, GO-term enrichment analysis
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This project will investigate artificial intelligence (AI) to improve weather forecasts and use crop models for making better farming decisions. The expected outcome is protocols for integrating AI