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-principles electronic structure calculations Perform materials screening including machine learning to identify promising thermoelectric materials for cooling technology The successful candidate is expected
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Computer science Engineering » Computer engineering Technology » Information technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 10 Feb 2026 - 23:59 (Europe/Oslo
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Knowledge about energy systems, especially the operational characteristics of renewable energy production (wind/solar) and batteries Knowledge and interest in applying AI/machine learning to time-series data
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models with drone imagery using machine learning techniques and data assimilation. The work will involve collaboration with an interdisciplinary team of researchers, engineers, and local stakeholders in a
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energy applications. You must have experience with data analysis, machine learning, or AI-supported methods applied to engineering or safety problems. PLEASE NOTE: For detailed information about what the
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selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral
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centered around a unique, open-source digital platform enriched with data and powered by domain knowledge-based advanced machine learning and artificial intelligence capabilities. By introducing a Digital
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, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier. Duties of the position Fundamental contributions in embodied AI
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democracy, our centre tackles the promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub
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collaborations across Norwegian universities, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics