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of mill and production operations. The scientific challenge will be to use the model and machine learning alongside live mill data (temperature, rolling loads etc) to reverse engineer the current
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
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benchmark chemometric and physics-informed machine learning models to monitor, forecast, and ultimately control critical process parameters, implanting these models in advanced control frameworks to optimize
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PhD Position - Operando X-ray Characterization of Catalytic Interfaces for Chemical Hydrogen Storage
, surface/interface science, hydrogen chemistry, and advanced characterization methods (essential). A strong motivation to learn in situ/operando X-ray methods and willingness to carry out experiments
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. This project proposes the development of a new CFD simulator for offshore renewable energy applications based on physics-informed deep learning that offers greater efficiency and robustness. This is a unique and
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analysis equipment and software and learn how ideas emerging from academic research can be developed towards commercial application. You will also undertake a secondment at the University of Liverpool’s
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advanced machine learning techniques Optical design and miniaturization of a novel spectroscopic sensor based on freeform optics, optimized for agrifood applications For this function, our Brussels
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, relating to craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation
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(preferably in Python, including deep learning frameworks such as PyTorch); affinity with medical image analysis and computational modeling; experience with neural networks for image analysis, generative
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Faculty of Arts The Faculty of Arts is committed to the development of knowledge with a strong scientific and social impact. With over 500 academic and support staff, we teach and conduct research