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extensive professional networking opportunities A structured PhD program with a comprehensive range of continuing education and networking opportunities - more information about the PhD program at the HZDR
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theoretical and/or computational research in Nonequilibrium Statistical Physics and Active Matter, under the supervision of Ramin Golestanian. For more information concerning our current areas of research
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will engage in theoretical and/or computational research in Nonequilibrium Statistical Physics and Active Matter, under the supervision of Ramin Golestanian. For more information concerning our current
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of the scientific themes of the RTG Collaborating with other project partners of the RTG Active participation in the structured training program of the RTG including ring lectures, research and PhD seminars
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Description The Chair of Applied Mathematics at the Faculty of Mathematics and Geography at the KU Eichstätt-Ingolstadt invites applications for a position as Doctoral candidate in Applied Mathematics/ Approximation Theory to be filled by the earliest possible starting date. The Chair of Applied...
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: university degree (Master or diploma) in physics, biology, computer science, or a related field excellent background in RDM principles and FAIR data practices, ideally gained in a scientific environment
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represent stakeholder preferences. The integrated Research Training Group (RTG) will provide doctoral researchers with an attractive qualification program, foster networking, enable internationalization and
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the information available in ptychography data About you: Master's degree in Physics/Mathematics/Computer Science or related subject Willingness to familiarize yourself in depth with new topics Good capacity
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individually, for example through training opportunities and the structured JuDocS program for doctoral candidates: https://www.fz-juelich.de/en/judocs . Health and well-being: Your health is important to us
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and performing laboratory (wind-wave facility) experiments, using state-of-the-art imaging techniques developing computational codes to process and understand large experimental datasets (e. g., image