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students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position A PhD position, fully funded for three years
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
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assess how effectively imaging techniques capture or enhance the taxonomic resolution of traditional net sampling. Training You will gain skills in plankton taxonomy, image processing, ecological
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of autophagy initiation and membrane dynamics by combining in vitro reconstitution, structure-function studies, biochemistry and cell biological approaches. We are seeking a highly motivated PhD candidate
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unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position A PhD position, fully funded for three years, is available at the Department
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processing techniques that take full advantage of these capabilities, in order to translate them into optimal radar performance. The purpose of the PhD is to lay down theoretical and practical foundations
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Description For our location in Hamburg we are seeking: PhD Position: Method Development for Hierarchical X-Ray Imaging at Synchrotron Facilities Limited: 2 years | Starting date: earliest possible
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PHD DEVELOPMENT AND EXPERIMENTAL VALIDATION OF UNDERGROUND SAND-BASED THERMAL ENERGY STORAGE SYSTEMS
Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Austria Application Deadline 28 Dec 2025 - 23:59 (Europe/Vienna) Type of Contract Temporary Job Status Part-time Is the job funded
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student (f/m/d) “neural basis of memory” (neurosciences) The PhD position is available for a fixed term appointment for 3 years, with a salary of TV-L E13 in part time 65% (26 hours/week) starting from
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? Then check out the vacancy below and apply for a PhD position in this exciting research direction. Join Us! Modern deep learning is progressing fast. Yet even the most advanced neural networks are paired