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to the Dean's office of the Faculty of Physics. 2. Acceptance by an authorised supervisor For further information, please visit our website: http://www.uni-goettingen.de/en/how-to-become-a-phd-student/501047.html
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international research community! Founded in 2007, GGNB is a joint enterprise between four faculties of the University of Göttingen (Biology, Chemistry, Medicine, and Physics), the Max Planck Institutes
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found at the following link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying Applicants can apply for a Scholarship from the University of Sheffield but should note that competition
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to apply Website https://jobrxiv.org/job/university-of-bologna-27778-phd-integrated-6g-localizat… Requirements Additional Information Work Location(s) Number of offers available1Company/InstituteUniversity
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structures; Knowledge of Romanian and English. LanguagesROMANIANLevelGood LanguagesENGLISHLevelGood Research FieldChemistryYears of Research Experience1 - 4 Additional Information Selection process https
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. Qualifications and terms of appointment: PhD in Physics, Astronomy, or related field (completed or expected by start date) Eligibility extends to current postdoctoral scholars Appointment term: 3 years Salary
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University of Texas Health Science Center San Antonio | San Antonio, Texas | United States | 4 days ago
PhD PROGRAM DIRECTOR The School of Nursing at UT Health San Antonio invites PhD (Doctor of Philosophy) in Nursing or related field to apply for a full-time research position as Associate Professor
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a PhD degree in physics or related field and demonstrate a strong track record of research in experimental condensed matter physics. Appointment at Associate Professor level or above will also be
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T.Vickey@sheffield.ac.uk or look at the University’s website: https://www.sheffield.ac.uk/postgraduate/phd. Further Information on the Particle Physics group in Sheffield and our projects can be found here
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information about the network: https://euraxess.ec.europa.eu/jobs/401249 1. Context and Challenges Title: Physics-Informed Neural Operators (PINO) for Ultra-Fast Tomography: Toward Fundamental and Generalizable