The Postdoctoral Researcher will work in the Systems Realization Laboratory (SRL) at Florida Institute of Technology (Florida Tech) on a National Science Foundation (NSF)-funded research project focused on co-design exploration for Integrated Computational Materials Engineering (ICME).

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 partners to advance next-generation ICME-enabled design frameworks. The researcher will also support project deliverables, publications, student mentoring, and related research activities.

Requirements:

• Advanced knowledge of decision-based design, multidisciplinary design optimization, robust design, co-design exploration, uncertainty quantification and management.
• Ph.D. in mechanical engineering.

The Postdoctoral Researcher will work in the Systems Realization Laboratory (SRL) at Florida Institute of Technology (Florida Tech) on a National Science Foundation (NSF)-funded research project focused on co-design exploration for Integrated Computational Materials Engineering (ICME).

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 partners to advance next-generation ICME-enabled design frameworks. The researcher will also support project deliverables, publications, student mentoring, and related research activities.

Requirements:

• Advanced knowledge of decision-based design, multidisciplinary design optimization, robust design, co-design exploration, uncertainty quantification and management.
• Ph.D. in mechanical engineering.