Employment Opportunities

The Air Vehicle Optimization Group at UDRI is a diverse team of skilled engineers and scientists who work closely with Air Force Research Laboratory (AFRL) to develop, prototype, and test numerical models, software, and hardware for the multidisciplinary design and analysis of aerospace vehicles. The group brings together specialties in Aerospace, Electrical and Mechanical Engineering, Computer Science, Mathematics, and Physics to combine state-of-the-art engineering tools with innovative ideas to support future aircraft design methods form conceptual design through flight testing. A few of the projects the group currently supports include: Computational Aircraft Prototype Synthesis (CAPS), Missile Enhancement via ReconfigurabLe Interceptor Nose (MERLIN), Efficient Supersonic Aircraft Vehicle (ESAV), X-56, and XQ-58A.

The group is seeking an Associate Research Engineer to support ongoing research and development contracts at Wright-Patterson AFB in the Air Force Research Laboratory providing basic and early applied research supporting the development of a scalable and holistic UQ framework that enables the simultaneous inclusion of multi-physics and multi-fidelity models as well as experimental data at varying levels of trust is necessary to address UQ in Multidisciplinary Analysis and Design Optimization (MADO).

The successful candidate will utilize their skills and experience to develop and apply methods for Uncertainty Quantification (UQ) in MADO, focusing on the early (conceptual and preliminary) design phases. Types of uncertainty that shall be considered may include, but are not limited to: parameter uncertainties, such as model or design parameters, geometric or material variables, and parameters associated with environment and process control; model uncertainties, such as from physics-based models from simple to complex, empirical models based on experiments, couplings/interfaces between disciplines, and model boundary conditions; data uncertainties, including noise, measurement errors, and missing data; requirements or usage uncertainty, including uncertainty in constraints; and, uncertainties arising from simulation, including discretization errors, round-off errors, and algorithmic errors. Methods for sensitivity analysis and machine learning applied to UQ are sought for effectiveness-based design (mission/operational analysis) and problems of transient / dynamic behavior where the states are stochastic due to the uncertain parameters (coefficients) within the equations of motion. Skills of interest, and program/software experience, include, but are not limited to:
a. Design and modeling, solid modeling, modeling using Engineering Sketch Pad (ESP) and Computational Aircraft Prototype Synthesis (CAPS)
b. Uncertainty quantification, reduced order models, surrogate models, probability, and statistics
c. Structural dynamics, aeroelasticity, mechanical vibrations, system dynamics & control
d. Design optimization, gradient-based optimization, reliability/robust design, sensitivity analysis, analytic sensitivities, adjoint sensitivities
e. Machine learning, neural networks, data-driven decision making
f. MATLAB, Abaqus, NASTRAN, ASTROS, SmartUQ, DAKOTA, DOT/BIG DOT

- Bachelor's or equivalent degree in Mechanical Engineering or related discipline from ABET accredited institution.
- Basic experience with Python, MATLAB, Abaqus, C++, SOLIDWORKS, Systems Tool Kit (STK), NASTRAN, ASTROS, SmartUQ, DAKOTA, DOT/BIG DOT or similar programming languages
- Knowledge of aircraft design and performance analysis
- Strong verbal and interpersonal skills
- Excellent attention to detail
- Proficient in MS Office Excel, PowerPoint, Word
- Due to requirements of our research contracts with the U.S. federal government, candidates for this position much be a U.S. Citizen
- The candidate must be able to pass a National Agency Check

While not everyone may possess all of the preferred qualifications, the ideal candidate will bring many of the following:

- Master's in Mechanical Engineering; SOLIDWORKS Associate's Certification for Mechanical Design
- Proficient using Python/MATLAB, Java, or similar programming languages
- Experience with NASTRAN/ABAQUS, ASTROS, SmartUQ, DAKOTA, DOT/BIG DOT
- Knowledge of CAPS
- Knowledge of machine learning, neural networks, data-driven decision making
- Knowledge of thermodynamics, heat transfer, and fluid dynamics
- Knowledge of engineering design and solid modeling
- Experience with structural dynamics, aeroelasticity, mechanical vibrations, system dynamics & control

Informed by its Catholic and Marianist mission, the University is committed to the principles of diversity, equity, and inclusion. Informed by this commitment, we seek to increase diversity, achieve equitable outcomes, and model inclusion across our campus community. As an Affirmative Action and Equal Opportunity Employer, we will not discriminate against minorities, women, protected veterans, individuals with disabilities, or on the basis of age, race, color, national origin, religion, sex, sexual orientation or gender identity.