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of thermal energy and (hybrid) machine learning in which physics-based models are combined with data-driven techniques. Thermal cycles make it possible to meet the demand for heating, cooling, and electricity
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only accurate, but also physically interpretable and uncertainty-aware. The PhD project will focus on fault diagnosis and root-cause identification for a web-winding machine, serving as an industrially
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researchers from IDLab-AIRO (robotic experts) and imec. Your main tasks include: Reviewing literature on decentralized control frameworks in the domain and machine learning algorithms compatible with
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estimates an appropriate price (pricing/revenue management). This is done through machine learning and data analytics techniques, making use of historical and product attribute data. Market-based valuation
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machine learning. This encompasses application domains such as industrial inspection, (ultra) high-definition video enhancement, smart multi-camera networks, computer vision, sensor fusion, and (medical
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researchers from IDLab-AIRO (robotic experts) and imec. Your main tasks include: Reviewing literature on decentralized control frameworks in the domain and machine learning algorithms compatible with
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engineering Researcher Profile First Stage Researcher (R1) Country Belgium Application Deadline 31 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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consistency outside the training domain. This PhD research is envisioned to result in a breakthrough in the application of machine learning methods to fire engineering problems, by ensuring compatibility with
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(preferably) in Computer Science, Electrical Engineering, or a related field. You have a strong background or interest in one or more of the following areas: computer vision, machine learning, signal processing
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concepts such as digital twins, learning from human interactions. In the domain of efficiency and sustainability, projects relate to a.o. the development of novel electrical machines, engineering of