2026 Summer Internship, Scientific Machine Learning for Fluid Dynamics

Job Description

2026 Summer Internship, Scientific Machine Learning for Fluid Dynamics | RTX

The Tone:
This is a Summer Internship at RTX, located in East Hartford, CT, operating under a hybrid work model. RTX is a leading Aerospace and Defense company that provides advanced systems and services globally, building solutions across diverse fields like quantum physics, electric propulsion, and hypersonics. This internship within the RTX Technology Research Center (RTRC) is essential for advancing the company’s capabilities in scientific machine learning for fluid dynamics, directly contributing to the development of next-generation aero-thermal and high-speed flow modeling.

The TL;DR
• Role: Internship
• Type: Seasonal/Temporary (Summer)
• Location: Hybrid – East Hartford, CT
• Pay: $37000–$82000 yearly
• Team: Dedicated working group within the RTX Technology Research Center, collaborating with a multidisciplinary team.
• Mission: Developing advanced modeling capabilities for aero-thermal and high-speed flows using scientific machine learning for fluid dynamics.

What You’ll Actually Do
• Develop: Advance modeling capabilities for aero-thermal and high-speed flows by applying computational fluid dynamics, machine learning, and data-driven techniques.
• Collaborate: Engage with a multidisciplinary team, integrating expertise from aerodynamics, aerothermochemistry, and measurement techniques to create comprehensive solutions.
• Analyze: Conduct creative analyses and rigorous problem-solving to contribute to the delivery of novel modeling capabilities and innovative technologies.
• Communicate: Present and publish research findings effectively through both verbal presentations and written publications to technical and non-technical stakeholders.

The Must-Haves
• Background: Currently pursuing a Bachelor’s or advanced degree in an applicable scientific or engineering discipline relevant to aerospace, applied mathematics, or computational science.
• Experience: Demonstrated expertise in the application of scientific machine learning to computational fluid dynamics, including practical experience with physics-informed machine learning, foundation models, and/or model discovery approaches.
• Skills: Scientific machine learning, Computational fluid dynamics, Physics-informed machine learning, Foundation models, Model discovery, Data-driven modeling.
• Bonus: Pursuing a Master’s Degree or Ph.D. in applied mathematics or aerospace engineering; adaptability and the ability to thrive in a collaborative environment; excellent communication skills, including informal and formal presentation abilities, and a talent for fostering positive relationships with internal and external partners; capability to manage uncertain requirements, develop effective approaches to solve complex problems, and recognize when additional subject matter expertise is required.