Research Engineer, Multi-Physics Modeling and Scientific Machine Learning

Insight Global is seeking a Research Engineer, Multi-Physics Modeling and Scientific Machine Learning for a leading energy technology research organization. This candidate will work at the intersection of computational physics, artificial intelligence, and high-performance computing to develop innovative modeling solutions for next-generation energy systems. The role focuses on applying scientific machine learning techniques to complex fluid dynamics and multi-physics problems, creating data-driven surrogate models that accelerate traditional simulation workflows while maintaining high levels of accuracy. The ideal candidate will have deep expertise in fluid mechanics, CFD, AI/ML, and HPC environments, along with a passion for advancing cutting-edge technologies in power generation, renewable energy, electrification, and other large-scale industrial applications. This is a highly visible research position offering the opportunity to collaborate with multidisciplinary teams and contribute to transformative energy innovations.

Day-to-Day: * Develop advanced multi-physics modeling methodologies * Execute high-fidelity simulations and computational analyses * Design and implement scientific machine learning solutions * Build AI-enabled surrogate models for complex spatiotemporal systems * Apply machine learning to fluid mechanics and flow physics challenges * Deploy and test algorithms on HPC clusters * Evaluate model accuracy and computational performance improvements * Conduct validation and verification studies * Collaborate with cross-functional engineering and research teams * Present findings to technical and non-technical stakeholders * Document technical approaches, results, and recommendations * Support next-generation energy technology development programs

• PhD in Mechanical Engineering, Aerospace Engineering, or related engineering/scientific discipline
• Strong foundation in fluid mechanics and flow physics
• Research experience in advanced computational methods for multiscale physics applications
• Demonstrated experience applying AI/Machine Learning to physics-based or engineering problems
• Experience with scientific machine learning methodologies
• Proficiency in Python
• Experience with PyTorch or similar ML frameworks
• Experience deploying algorithms on High-Performance Computing (HPC) clusters
• Experience with CFD, aerodynamics, turbulence modeling, heat transfer, combustion, reacting flows, or related fluid dynamics disciplines
• Experience with engineering simulation tools such as ANSYS or COMSOL
• Ability to quantify computational acceleration and accuracy improvements from AI-enabled models
• Strong communication and technical documentation skills
• Ability to work within multidisciplinary research teams
• Experience with JAX
• Experience with Fortran and/or C+- Physics-Informed Neural Networks (PINNs)
• Data-driven surrogate modeling
• High-fidelity CFD and multi-physics simulation
• Experience with industrial-scale energy systems
• Gas turbine, wind turbine, renewable energy, nuclear, or electrification experience
• Experience with high-temperature and high-pressure flow systems
• Published research, conference papers, or postdoctoral experience
• Experience with reactive flows and combustion systems
• Familiarity with complex geometries such as turbine blade flows