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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts
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the attractiveness to the users, we need innovative designs where fixed and flexible services support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and
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these sounds fascinating, then this PhD position is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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Innovative Computer Architecture group and become part of the CogniGron research centre as well as the NL-ECO project. Where to apply Website https://www.academictransfer.com/en/jobs/354943/phd-in-simulators
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-Design Methodology We are seeking a highly motivated PhD candidate to join the TACIT project, focusing on accessibility, disability studies, (co-)design methods, and Human-Computer Interaction (HCI
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explore how artificial intelligence can be used to predict the response to therapy in cases of acute rejection of the kidney graft. Using machine-learning techniques, you will assess kidney transplant
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machine learning to improve outcome prediction and patient stratification. deepen our understanding of the etiology, clustering, and diagnostics of cardiovascular diseases in critically ill patients
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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insights for best practices and design guidelines. Collaborate across the TACIT project: Collaborate with other PhD candidates and researchers working on the project to share insights and learn from its