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20th May 2026 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD position. PhD Candidate in Integrated Predictive Maintenance in Wind Energy Apply
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Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Experience with predictive maintenance, fatigue, fault detection
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Bayesian prediction models with uncertainty quantification for trustworthy personalized treatment decisions in the T-PRESS Evidence Ecosystem Framework”. The primary objective of the T-PRESS consortium is to
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Sustainable Healthcare Decisions. About the project The fellowship period is 3 years and devoted to carrying out a project entitled “Reliable Bayesian prediction models with uncertainty quantification
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- and risk optimal installation and operation of offshore wind farms depend on accurate forecasts and predictions of environmental conditions and response of vessel and auxiliary systems. The focus
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within the research project SenseWood – Digitalisation of wooden building skins for a predictable performance. SenseWood, funded by The Research Council of Norway, aims to develop a new digital technology
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to transfer and predict high-resolution, small-scale snow characteristics onto larger spatial extents. Building on existing pilot drone data and an in-situ snow monitoring system operated by the Climate
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. This project aims to uncover fundamental "algorithms" the mammalian brain uses to predict or choose between dynamically moving agents, working at the intersection of ethology, computational behavior and systems
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on the combination of Reinforcement Learning (RL) and Model Predictive Control (MPC). It will build up upon the work done at ITK on the topic. Several research focuses are considered: verification pathways in RLMPC
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with over 60 companies. The research will focus on the combination of Reinforcement Learning (RL) and Model Predictive Control (MPC). It will build up upon the work done at ITK on the topic. Several