Machine Learning for Fluid Dynamics Summer Internship

RTX•East Hartford, CT

5d•Hybrid

About The Position

RTRC invites individuals with scientific machine learning expertise and comprehensive understanding of computational fluid dynamics to apply for a 2026 Summer Internship position. We are seeking an intern to join a working group of research engineers who are focused on developing advanced modeling capabilities for aero-thermal and high-speed flows. The successful candidate will collaborate within a multidisciplinary team, including expertise in aerodynamics, aerothermochemistry, computational fluid dynamics, measurement techniques, machine learning, and data-driven modeling. The candidate is expected to contribute to the delivery of novel modeling capability and innovative technologies through creative analysis and problem solving; to possess the ability to engage and collaborate in a fast-paced multi-faceted environment; to communicate research results effectively through both verbal presentation and written publications.

Requirements

Requires a High School Diploma or equivalent and student must be pursuing a Bachelor’s or advanced degree in an applicable discipline.
U.S. Person (U.S. citizen, permanent resident, or granted refugee or asylee status) as defined here: https://www.ecfr.gov/current/title-22/chapter-I/subchapter-M/part-120/subpart-C/section-120.62
Expertise in scientific machine learning applied to computational fluid dynamics.
Experience with physics-informed machine learning, foundation models, and/or model discovery.

Nice To Haves

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, informal and formal presentation skills, and ability to foster positive relationships with internal and external partners.
Ability to manage uncertain requirements, develop approaches to solve problems, and recognize when additional subject matter expertise is needed.

Responsibilities

Collaborate within a multidisciplinary team to develop advanced modeling capabilities for aero-thermal and high-speed flows using computational fluid dynamics, machine learning, and data-driven techniques.
Conduct creative analyses and problem-solving to contribute to the delivery of innovative modeling technologies and solutions.
Communicate research findings effectively through presentations and written publications to both technical and non-technical audiences.