Summer 2025, Intern: Machine Learning for Fluid Dynamics Full-time

Date Posted:
2024-10-21
Country:
United States of America
Location:
UT13: RC-CT – Corp 411 Silver Lane, East Hartford, CT, 06108 USA
Position Role Type:
Remote

RTX is an Aerospace and Defense company that provides advanced systems and services for commercial, military, and government customers worldwide. It comprises three industry-leading businesses – Collins Aerospace, Pratt & Whitney, and Raytheon. Its 195,000 employees enable the company to operate at the edge of known science, as they imagine and deliver solutions that push the boundaries in quantum physics, electric propulsion, directed energy, hypersonics, avionics, and cybersecurity.

To realize our full potential, RTX is committed to creating a company where all employees are respected, valued, and supported in the pursuit of their goals. We know companies that embrace diversity in all its forms not only deliver stronger business results, but also become a force for good, fueling stronger business performance and greater opportunity for employees, partners, investors, and communities to succeed.

The following position is to join our RTX Technology Research Center:

RTX Technology Research Center (RTRC) serves as the innovation hub for RTX. We conduct basic and applied research in a stimulating multi-disciplinary environment where scientists, engineers, practitioners, and subject matter experts collaborate and exchange experience. By combining a passion for science and research with precision engineering we create smart and sustainable solutions and products that help our businesses shape the future.

RTRC invites individuals with machine learning expertise and comprehensive understanding of computational fluid dynamics to apply for a 2025 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.

What You Will do

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.

Qualifications You Must Have

• Currently enrolled in either a Master’s or Ph.D. degree program in mechanical or aerospace engineering, or closely related field. Please submit a copy of your academic transcripts with your application.
• 1+ years of experience in machine learning applied to computational fluid dynamics and turbulence modeling.
• 1+ years of experience with physics-aware machine learning, data assimilation, and/or model discovery.
• U.S. Person (U.S. citizen, permanent resident, refugee or asylee) or eligible to obtain necessary export authorizations required. https://www.ecfr.gov/current/title-22/chapter-I/subchapter-M/part-120/subpart-C/section-120.62

Qualifications We Prefer

• 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.

Learn More & Apply Now!
Location: This position can be hybrid in East Hartford, CT or remote.
Please consider the following role type definitions as you apply for this role:
Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.
Remote: Employees who are working in Remote roles will work primarily offsite (from home). An employee may be expected to travel to the site location as needed.

The salary range for this role is 37,000 USD – 82,000 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills.

Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement.

Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company’s performance.

This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply.

RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window.

RTX is An Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age or any other federally protected class.

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