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computational modeling to identify bacterial strains and metabolites that promote or hinder probiotic establishment. By combining multi-omics data with systems biology and machine learning approaches
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profile for their ideal candidates are described as follows. PREMAL is a project focused on privacy-preserving machine learning using FHE. The project will investigate trade-offs between accuracy, time, and
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of applications in image analysis and machine learning, as well as in digital signal processing and acoustic imaging. There are about 20 Postdocs and PhD research fellows in the group with financing
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computer science or machine learning with a specialization in natural language processing Documented knowledge of core machine learning methods and techniques, including at least basic experience with deep neural
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convergence of high-performance computing (HPC) and AI, which is a subject that sees an increasing importance due to the widespread use of AI and in particular machine learning (ML). As today’s mainstream AI/ML
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on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/289326
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-fellow-in-deep-learning-for-subsurface-imaging Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/290391/postdoctoral-research-fel… Requirements Research FieldComputer scienceEducation
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understanding of how acoustic waves are generated and transmitted in wells. The LeDAS project aims to overcome these challenges by combining physical modelling, advanced signal processing, and machine learning in
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dissertated before the start-up date of the position. A research profile with relevant experience in biological sequence analysis, with complementary skills in machine learning or other relevant algorithms. A
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by the Research Council of Norway and the partners for the period 2026-2030. There will be approximately 25 doctoral scholarships and some postdoc positions. More information can be found here: https