281 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Sweden
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analysis, statistical modelling, linear mixed models, and machine learning among others. The position is well suited for an individual interested in quantitative genetics and data analysis that wishes
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(e.g., power electronics or machine learning applications in power systems). The PhD degree must have been awarded no more than three years prior to the application deadline*. The ideal candidate has
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engineering, or a related subject area. The applicant should have solid experience in computational electromagnetism/photonics, inverse design, or machine learning. You will be expected to take initiative, work
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Deadline 31 Dec 2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within
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groups working towards a common goal. For this postdoc project, we seek a dynamic and motivated candidate with an interest in computational electromagnetism, inverse design, and/or machine learning in
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) Country Sweden Application Deadline 19 Dec 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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, through the development of new materials to direct industrial projects generating new inventions. We have a strong learning commitment on all levels from undergraduate to PhD studies where physics meet
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hardware and radio systems to distributed cloud platforms and applications. This broad expertise enables research that seamlessly connects connectivity, computation, and cloud-native design. To The Division
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for developing theoretical skills and for learning new computational techniques and statistical approaches. Research questions that are currently pursued in our group include the applicability of ab initio