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allocation mechanisms, with a particular focus on the efficiency and fairness of Eurotransplant’s liver allocation system. Information We are looking for a strong, motivated PhD-student with an analytical
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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on causal and mechanistic studies of microbiome-mediated pathogenesis. This is achieved by bridging microbiology and big data analytics in a structured doctoral training environment. The need of microbiome
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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to incorporate additional field data. This PhD project offers a great opportunity to work with large-scale biodiversity and climate datasets, develop strong analytical skills, and collaborate with international
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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: • Image processing and sensor data analysis, ideally with HSI or other imaging technologies. • Machine learning and artificial intelligence, especially classification and segmentation tasks. • High
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implications of AI-enabled conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in
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. This issue can have safety implications, particularly in closed-loop setups. Physically Informed Machine Learning (PIML), and in particular Physics-Informed Neural Networks (PINN), are less dependent on data