132 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions in Norway
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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acquire this within two years of appointment. Competence in quantitative research methods, particularly longitudinal research designs and analysis, is considered an advantage. Much of the thesis advice
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machine learning framework that integrates diverse biological data—including transcription factor (TF)–DNA interactions, epigenomic features, and three-dimensional (3D) genome organization—to identify
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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interpretable machine learning framework that integrates diverse biological data—including transcription factor (TF)–DNA interactions, epigenomic features, and three-dimensional (3D) genome organization
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, usually in the form of teaching and/or supervision duties. In this case, Postdoctoral fellows who are appointed for a period of four years are expected to acquire basic pedagogical competency in the course
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November 2026 at 23:59 CET Expected start: 1 January 2027, or upon agreement About REGULAIRE The scholarship is part of REGULAIRE (Regulatory Learning for the Governance of Transformative Technologies), a
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Business School Application deadline: 15 November 2026 at 23:59 CET Expected start: 1 January 2027, or upon agreement About REGULAIRE The scholarship is part of REGULAIRE (Regulatory Learning
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representation through machine learning. The position is for a fixed term of 3 years and is part of the project “Reaching AI Projections Trustworthy for Unseen Rainfall Extremes (RAPTURE)”, funded by a European
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal