32 data-"https:"-"https:"-"https:"-"https:"-"OsloMet---storbyuniversitetet" Fellowship positions at University of Bergen
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27th May 2026 Languages English English Norsk Nynorsk English PhD Research Fellow in Data-Driven Computational Mechanics for Offshore Wind Energy Apply for this job See advertisement UiB - Knowledge
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of the scholarly works on which the committee should place special emphasis, is attached in its entirety Two references (name and contact information) General information: For further information please contact
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the intersection of computer and life science. CBU has members with diversecompetences including experts in bioinformatics, biology, mathematics, computer science, chemistry, and biomedicine. CBU’s primary focus is
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discharges through modeling and analysis of data from the groundbreaking ALOFT flight campaign. Our group's recent publications revealed FGFs as a new class of high-energy atmospheric radiation, yet their
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into account when ranking the candidates. The names and contact information for two referees. One of these should be the main advisor for the master's thesis or equivalent thesis CV Transcripts and diplomas
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these will be taken into account when ranking the candidates. the names and contact information for two referees. One of these should be the main advisor for the master's thesis or equivalent thesis CV
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at prestigious international conferences. The PhD project is part of 15 GRAIL PhD projects Europe-wide out of which 2 will take place at the University of Bergen. More information about GRAIL can be found
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information about GRAIL can be found at grail.physik.tu-dortmund.de. The successful candidate will benefit from extensive interaction opportunities with several groups within the Horizon Europe GRAIL network
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campaign and the subsequent data exploitation is expected. We expect the findings to appear in top-tier peer-reviewed journals and to be presented at prestigious international conferences. The successful
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the use of modern machine‑learning methods within applied mathematics—particularly physics‑informed learning, anomaly detection, data‑driven modelling, and the construction of surrogate models grounded in