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traffic flow theory and simulation. You are a machine learning enthusiast (and realist). You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#. You can present and communicate your
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PhD candidates, postdoctoral researchers, and scientific and technical staff at HFML-FELIX working on related topics. You may also contribute to teaching activities within Radboud university and support
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is an advantage. Experience with programming (e.g., R, Python) and statistical analysis is highly desirable. Motivation to work in a collaborative, interdisciplinary research environment and good
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analytical techniques affinity with numerical modelling (Python, R, Aspect) excellent ability to communicate in both written and spoken English good social skills. As a university, we strive for equal
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on these materials. You will work in a collaborative and interdisciplinary environment, interacting with PhD candidates, postdoctoral researchers, scientific and technical staff, and contributing to joint experimental
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Holocene lowlands, and translation them into subsurface maps, sections, and schematizations; Proficiency in numerical groundwater modelling, including the use of coding (e.g. Python) and incorporation
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to three dimensions based on Virtual Leaf3D currently under development by a postdoctoral researcher in our team Publication of the results in the scientific literature and writing a PhD thesis. Co-teaching
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observations. You will work closely with a postdoctoral researcher who will lead the detector measurements and telescope deployment activities. The project is embedded within the Experimental Astronomy group and