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Field
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect
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candidate will investigate novel sensing methodologies, develop physics-informed computational models, and apply state-of-the-art machine learning techniques to extract meaningful information from complex
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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19 Aug 2026 Job Information Organisation/Company KU LEUVEN Research Field Engineering » Electrical engineering Engineering » Electronic engineering Engineering » Computer engineering Computer
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spectra. Validation of machine learning models on experimental Q-OCT signals. Development of neural-network-based methods for improving the resolution of Q-OCT images. Specific Requirements Description
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machine learning, embedded systems, and edge intelligence Hands-on experience on Nvidia Jetson boards or other edge platforms Strong knowledge in deep learning, particularly large language models or multi
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on the field can be derived from first principles, and how these constraints can improve the technique's performance, particularly when embedded in modern machine learning models. The ultimate goal is to
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of machine-learning-infused atomistic modeling techniques and their application to important problems in chemistry, physics and materials science. Together, you will help advance a key scientific
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, especially in quantitative subjects • Strong Python skills and experience with deep learning frameworks, preferably PyTorch • Solid foundations in machine learning, statistics, linear algebra, and model