R&D Leader - Advanced Modeling & Simulation

Description

Lead Owens Corning’s Advanced Modeling & Simulation team to shape the future of digital engineering by integrating AI-driven modeling, predictive simulations, and digital twin ecosystems, pioneering capabilities that accelerate innovation, enable autonomous optimization, and deliver sustainable solutions across enterprise R&D.

Provide strategic leadership by defining a modeling vision that includes multi‑physics simulations, reduced‑order models, and hybrid AI/physics workflows; build a high‑performing senior engineering team; drive technical excellence across AI‑simulation architecture and digital twins; collaborate with Materials Science, Glass Sciences, Product Development, Capital Delivery and Operations; manage project prioritization and resource allocation; and engage external resources to accelerate learning.

• Define and execute a modeling vision covering multi‑physics, reduced‑order, and hybrid AI/physics workflows.
• Build and lead a high‑performing senior engineering team with an agile, growth‑mindset culture.
• Oversee AI‑plus‑simulation architecture, digital twin ecosystems, and physics‑informed machine learning models.
• Partner with Materials Science, Glass Sciences, Product Development, Capital Delivery, and Operations to broaden modeling impact.
• Prioritize modeling projects based on business objectives and allocate resources to advance the strategy.
• Leverage external collaborations to accelerate learning and capability development.
• Apply physics‑based, data‑driven, and hybrid modeling approaches to translate product needs into insights.
• Conduct multi‑scale, multi‑physics simulations (structural, fluid, thermal, moisture, chemical) using CFD and FEA.
• Develop real‑time digital twins integrating IoT data, predictive analytics, and advanced control processes.
• Manage the full lifecycle of digital twins from design through operation to continuous optimization.
• Utilize CAE tools such as ANSYS, COMSOL, Abaqus, and GFM, as well as cloud‑based simulation platforms.
• Integrate machine learning techniques for surrogate modeling, optimization, and anomaly detection.

Requirements

Require advanced knowledge of physics‑based, data‑driven, and hybrid modeling; ability to translate process and product needs into actionable insights; strong background in multi‑scale, multi‑physics simulations (structural, fluid, thermal, moisture, chemical) and techniques such as CFD and FEA; expertise in digital twin development, real‑time data integration, IoT connectivity, lifecycle management, and advanced controls; proficiency with CAE tools (ANSYS, COMSOL, Abaqus, GFM) and cloud simulation platforms; and skill in applying machine learning for surrogate modeling, optimization, and anomaly detection.

Hold an advanced degree in Mechanical, Civil, Chemical, or Materials Engineering; possess over 10 years of modeling and simulation experience with technical leadership; demonstrate expertise in CAE tools and AI/ML integration; have a proven track record in digital twins, surrogate modeling, and Simulation Data Management; and understand engineering design, safety standards, and integrated system design applied to manufacturing and product development.

• Hold an advanced degree in Mechanical, Civil, Chemical, or Materials Engineering and 10+ years of relevant experience.
• Demonstrate expertise in Simulation Data Management, including storage, organization, and result sharing.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance