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University Medical Center Utrecht (UMC Utrecht); yesterday published | Netherlands | about 6 hours ago
This is a full-time post-doctoral position (1.0 FTE), initially for 1.5 years, and will be embedded within the UMC Utrecht AI-Lab initiative. Your primary focus will be the development of AI models
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practices; Supervising and mentoring bachelor and master students involved in the research; Writing and publishing high-quality scientific articles in leading international journals; Presenting results
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At the Julius Center for Health Sciences and Primary care, one of our key research areas is the relationship between acute infections and non-communicable diseases (e.g. cardiovascular, auto-immune
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environments. Information This research focuses on advancing cutting-edge aerodynamic design methodologies to significantly enhance wind energy harvesting in urban settings. The primary objective is to develop a
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Sciences and Primary care, one of our key research areas is the relationship between acute infections and non-communicable diseases (e.g. cardiovascular, auto-immune), using large-scale real-world data
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Amsterdam UMC Research BV supports non-profit scientific research. In doing so, we provide researchers with everything they need to excel. Our principal investigators (PIs) and project leaders offer
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retrieval. As a postdoctoral researcher, you will be part of the computational component of the project. You will collaborate closely with the principal investigator, two PhD candidates (working in
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employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20. Additional employment conditions Work and science require good employment practices. Radboud University's primary
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energy harvesting in urban settings. The primary objective is to develop a high-fidelity CFD–machine learning (CFD–ML) framework capable of efficiently analyzing and optimizing rooftop aerodynamic duct
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ideas. Within the Psychological Methods group, you will work in the team of EJ Wagenmakers. One of the main goals of this team is to develop and promote Bayesian inference (and in particular Bayes factor