Graduate Co-op - Computational Fluid Dynamics

Analytical Mechanics Associates; California (MO); On-site; USD 40,000 - 46,000; Part time

Benefits offered by this job

Tuition reimbursement

Vision Insurance

Long-term disability

401(k)

Job summary

Analytical Mechanics Associates (AMA) seeks a Graduate Co-op to support NASA Ames Research Center (ARC) in Moffett Field, CA, focusing on GPU-accelerated combustion modeling within the LAVA CFD solver. The role emphasizes implementing, optimizing, and validating turbulent combustion capabilities in a Linux-based HPC environment using C/C++, CUDA, MPI, OpenMP, and profiling tools.

The position targets a Ph.D.

Qualifications

• Ph.D. student in a technical field; strong combustion flow modeling background.

Responsibilities

• Collaborate with CFD experts to integrate GPU enhancements into solvers like Cart3D and LAVA.

Skills

C/C++

CUDA

GPU programming

MPI/OpenMP

Linux HPC

CFD

Combustion modeling

GitLab CI/CD

Nsight profiling

Education

Ph.D. in Computer Science / Aerospace / Mechanical Engineering

Tools

CUDA Toolkit

Nsight Systems

Nsight Compute

MPI

Job description

Graduate Co-op - Computational Fluid Dynamics

vision insurance, long term disability, tuition reimbursement, 401(k)

United States, California

Aug 17, 2026

Job Description

Analytical Mechanics Associates (AMA) is seeking a highly motivated and detail-oriented Graduate Co-op to support NASA Ames Research Center (ARC) in Moffett Field, CA, under the Aircraft Systems and Spaceflight Engineering Support Services (ASSESS) contract. The position will support the Computational Aerosciences Branch (TNA).

This part-time co-op position is designed for a graduate student currently pursuing a Ph.D. with a strong background in combustion flow modeling, high-performance computing (HPC), and GPU programming. The co-op will contribute to the implementation, optimization, and verification/validation of turbulent combustion capabilities in NASA's LAVA computational fluid dynamics (CFD) solvers, enhancing simulations critical to aerospace research.

About the Project

The Computational Aerosciences Branch (TNA) at NASA Ames Research Center develops and applies advanced computational tools for aerospace applications. This position focuses on implementing and optimizing GPU-resident combustion modeling capabilities in the LAVA CFD solver. The successful candidate will work on improving a highly robust and accurate numerical scheme for combustion flow simulation, utilizing a sophisticated software stack including C/C++, CUDA, MPI, OpenMP, and NVIDIA profiling tools in a Linux-based HPC environment.

Responsibilities

• Collaborate with CFD experts to integrate GPU enhancements into solvers such as Cart3D and LAVA.


• Conduct hardware-level GPU optimizations, including occupancy tuning, register usage, memory hierarchy improvements, and asynchronous stream utilization.


• Improve I/O performance, including potential compression and in-situ analysis.


• Design and implement a GPU-accelerated library tailored to linear solvers for implicit CFD analysis.


• Profile and analyze performance using tools such as Nsight Systems and Nsight Compute to ensure efficient use of GPU resources.


• Document work and present progress to the Computational Aerosciences Branch team.

Desired Skills

• Expertise in fluid dynamics and computational fluid dynamics, with an understanding of numerical schemes and their robustness and dissipation properties.


• Strong expertise in combustion flow physics and combustion flow modeling, including knowledge of subgrid-scale models for turbulent combustion such as thickened-flame and flamelet methods.


• Familiarity with canonical benchmark configurations for turbulent combustion simulations.


• Strong proficiency in C/C++.


• Knowledge of CUDA and GPU programming.


• Knowledge of MPI and OpenMP for parallel computing.


• Experience with Git and GitLab for version control and CI/CD pipelines.


• Proficiency in Linux-based systems and HPC supercomputing environments.


• Understanding of GPU memory hierarchy and optimization techniques.

Required Qualifications

• Currently enrolled in a Ph.D. program in Computer Science, Aerospace Engineering, Mechanical Engineering, or a related technical discipline.


• Expertise in combustion physics and modeling.


• Programming proficiency in C/C++ and CUDA.


• Strong communication and collaboration skills.

Salary Range: $29.00-$33.50/hour

U.S. CITIZENSHIP REQUIRED

Due to the requirements associated with this position and work performed at NASA Ames Research Center, candidates must be U.S. citizens .

Analytical Mechanics Associates (AMA) is proud of our customer relationships, our diverse and dynamic work environment, and our employees' career satisfaction. AMA is a small business with a wide reach; headquartered in Hampton, VA, AMA has operations in Greenbelt, MD; Huntsville, AL; Dallas and Houston, TX; Denver, CO; Mountain View, CA; and Edwards Air Force Base, CA. With over 60 years of experience, AMA specializes in aerospace engineering, science, analytics, information technology, and visualization solutions. AMA combines the best of engineering, science, and mathematics capabilities with the latest in information technologies, visualization, and multimedia to build creative solutions. We offer competitive salaries and a substantial benefits package, including but not limited to paid personal and federally recognized holiday leave, salary deferrals into a 401(k)-matching plan with immediate vesting, tuition reimbursement, short/long term disability plans, and a variety of medical, dental, and vision insurance options.
AMA is committed to the professional growth of every employee, understanding that the successes of our employees drive our success. We provide a work environment that is engaging, collaborative, and supportive. To learn more about our company, please visit our website at www.ama-inc.com/careers and follow us on Facebook and LinkedIn.

AMA is an Equal Opportunity Employer and does not discriminate against any applicant for employment or employee because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic prohibited under federal, state, or local laws.

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