Boeing Engineering & Technology Innovation Graduate Researcher Program, Applied Mathematician Intern

Boeing•North Charleston, SC

2d•Onsite

About The Position

The Graduate Researcher Program (GRP) is a unique 10-12 week internship program designed specifically for students pursuing their PhD. GRP is a hybrid experience of real-world industry work experience combined with development programming that exposes students to Boeing, its products, services, and its people. GRP focuses on deeply connecting students to their team and Boeing through work experience across multiple summers with the goal of ultimately offering a full-time position at the conclusion of academic studies if a mutual fit is achieved. The Applied Mathematics group at Boeing is the company’s most concentrated community of mathematicians, driving innovation across the enterprise through advanced research, algorithm development, and technical consulting. As part of Engineering & Technology Innovation (E&TI), the team applies cutting-edge mathematical methods—including geometry optimization, computational modeling, machine learning, and statistical analysis—to solve complex aerospace challenges and shape future capabilities. The Applied Mathematics team collaborates across multidisciplinary engineering domains, translates theoretical concepts into robust algorithmic implementations, and influences the adoption of mathematical tools that enable safer, more efficient, and more advanced aerospace systems. The successful candidate will gain hands-on experience developing novel mathematical solutions, contributing to research proposals, and transitioning technical insights into impactful models, software, and decision-making tools used across Boeing.

Requirements

Completed the first year of PhD studies by July 2027
Ability to work full-time for 10-12 weeks during Summer 2027
Pursuing a PhD in Applied Mathematics, Statistics, Computational Science, Engineering, Data Science, Machine Learning or a closely related field
Strong background in statistics, numerical methods, and data analytics
Demonstrated ability to apply mathematical methods to real-world problems
Experience with designing and analyzing experiments or simulations
Experience with uncertainty quantification and probabilistic modeling
Proficiency in programming for data analysis and scientific computing (e.g., Python, R, C/C++)
Ability to communicate effectively with both technical and non-technical audiences
Must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a “U.S. Person” as defined by 22 C.F.R. §120.62 is required. “U.S. Person” includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee.

Nice To Haves

Familiarity with software for scientific computing or simulation (e.g., numpy / scipy , PyTorch /TensorFlow/ML frameworks, JAX, and/or domain-specific tools)
Experience with building and validating predictive models, simulations, and data-analysis workflows with real-world aerospace datasets
Knowledge of uncertainty quantification methods (e.g., Bayesian inference, surrogate modeling, polynomial chaos, Gaussian processes)
Experience with high-performance computing, parallel processing, or scalable data pipelines
Familiarity with optimization, decision analytics, and digital twin concepts
Experience with one or more of the following: statistics software (R), scientific computing libraries (NumPy/SciPy), and programming in Python or R
Exposure to multi-disciplinary or multi-physics modeling is a plus

Responsibilities

Develop novel mathematical solutions.
Contribute to research proposals.
Transition technical insights into impactful models, software, and decision-making tools used across Boeing.