28 phd-in-power-electronics Fellowship positions at UiT The Arctic University of Norway
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science Mathematics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 22 Oct 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per
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Stig Brøndbo 22nd October 2025 Languages English English English Faculty of Science and Technology PhD Fellow in Knowledge-Driven Machine Learning Apply for this job See advertisement The position A
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strategic priority areas. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/286639/phd-fellow-in-computer-sc… Requirements Research FieldComputer scienceEducation LevelMaster Degree
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Stig Brøndbo 26th October 2025 Languages English English English Faculty of Science and Technology PhD Fellow in Computer Science - Accurate and Scalable Simulation of Edge Systems Apply
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Stig Brøndbo 2nd November 2025 Languages English English English Faculty of Science and Technology PhD Fellow in terrestrial Quaternary and glacial geology Apply for this job See advertisement
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Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 2 Nov 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is the job funded
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spaceborne altimeter and SAR sensors have been operating together. A PhD Student on WPs 1 & 2 in the project will match these ITDs to maps of sea ice deformation and linear kinematic feature (LKF) evolution
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completed PhD degree must be submitted before commencement. The following skills and attributes will be considered an advantage: Independence and self-motivation Creativity and ability to think outside
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Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 20 Oct 2025 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image