The researcher will contribute to the development of advanced methodologies for engineering design by investigating decision-based design strategies, multidisciplinary design optimization workflows, and data-driven modeling approaches. Research activities will include the development and application of surrogate modeling techniques (e.g., Gaussian Processes), Bayesian methods, and Reinforcement Learning strategies to address complex, coupled engineering design problems involving materials, products, and manufacturing processes. The position will involve computational modeling, algorithm development, design exploration, uncertainty quantification, and collaboration with graduate students and research pa...