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Field
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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The research project focuses on advancing seismic resilience strategies for industrial facilities by integrating and validating base isolation techniques. The doctoral candidate will numerically and
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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than conventional structures. Superstructure optimization, which uses a predefined architecture with numerous unit operations and possible paths, offers an alternative for identifying non-intuitive
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operation costs significantly. Besides, there is an opportunity to explore the commercialisation paths of the developed smart sensor prototype. You will gain from the experience in numerous ways, whether it
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deploy these technologies in the industry context without the need for big datasets. You will gain from the experience in numerous ways, whether it be transferable skills in the technical area of
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analysis, UV-Vis and fluorescence spectroscopy, nitrogen adsorption–desorption isotherms at 77 K, thermogravimetry (TGA), perfilometry and electrical measurements, also including knowledge in data
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., flanges, elbows, nozzles) under natural hazard-induced loading. Design and supervise experimental tests (axial and bending) to study Loss of Containment (LOC) conditions. Derive analytical/numerical leakage
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. Strong knowledge of quantitative and/or computational research methods, ideally in econometric analysis or optimization and simulation models. Preferable knowledge in Python and STATA. A collaborative team
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, Applied Mathematics, or a related field. Strong foundation in computational modelling & numerical simulations The laboratory The Decision and Bayesian Computation (DBC) – Epiméthée (EPI) laboratory