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NTNU 30th April 2025 Languages English English English We are looking for a PhD Candidate in Computational Modelling of Thin-Film Magnetic Materials Apply for this job See advertisement This is NTNU
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for antibiotics against Pseudomonas aeruginosa and other Gram-negative bacteria", funded through the FRIPRO programme from the Research Council of Norway. The main objective of this project is to develop potent
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"Validating fatty acid synthesis enzymes as targets for antibiotics against Pseudomonas aeruginosa and other Gram-negative bacteria", funded through the FRIPRO programme from the Research Council of Norway
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of this position is to develop a massively parallel version of a computer code called Commander, and apply this to archival data from Planck HFI, new data from Simons Observatory, and simulated data from LiteBIRD, a
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projects will work towards this goal. PhD research fellow will be part of the PhD programme in Computer Science: Software Engineering, Sensor Networks and Engineering Computing (https://www.hvl.no/en
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programme is a prerequisite for employment, and the programme period starts on commencement of the position. The PhD fellow position is for a period of three years and full time studies with the possibility
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focus on how interactions between species at different trophic levels shape these responses. The project combines (1) analysis of long-term datasets to quantify historical changes in the distributions
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or theoretical/computational physics. Master’s students may apply if they expect to complete their final examination by 01.10.2025. Employment is contingent upon the degree being awarded. Solid experience with
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geophysics or a closely related field such as applied mathematics or theoretical/computational physics. Master’s students may apply if they expect to complete their final examination by 01.10.2025. Employment
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significant contribution towards this goal, but many environmental, economic, and societal factors need be considered. This PhD position will focus on assessing the future evolution and spatial distribution