Job DescriptionThis is a highly technical, hands-on role for someone who wants to solve difficult, multidisciplinary aerospace problems rather than incrementally improve established designs.
- Lead aerodynamic and aeroacoustic analysis and optimization of MagLev Aero's rim-driven electric and hybrid ducted fan systems.
- Model and predict fan, rotor, duct, and integrated propulsion-system aerodynamic and acoustic performance.
- Develop designs that simultaneously optimize thrust, efficiency, noise, weight, and overall system performance.
- Apply CFD, computational aeroacoustics, reduced-order modeling, optimization, and other advanced simulation methods.
- Work with MAGGIE, MagLev Aero's Multiphysics Aerospace Generative Graphical Inference Engine, and advanced AI tools to accelerate design exploration and optimization.
- Develop and improve physics-based and data-driven models used within MagLev's multidisciplinary design environment.
- Correlate analytical and computational models with experimental and test data.
- Define test requirements and work with engineering teams to validate aerodynamic and acoustic performance.
- Collaborate closely with propulsion, electrical, mechanical, controls, motor, thermal, and systems engineers.
- Identify unconventional technical approaches and translate them into designs that can be built, tested, and ultimately commercialized.
- Serve as a technical authority in aerodynamics and aeroacoustics as MagLev advances its propulsion technology toward flight applications.
Qualifications- Advanced degree in Aerospace Engineering, Mechanical Engineering, Acoustics, or a closely related field. PhD strongly preferred.
- Deep expertise in aerodynamics and aeroacoustics, ideally involving propellers, fans, rotors, turbomachinery, ducted fans, eVTOL, or advanced propulsion systems.
- Strong background in CFD and computational modeling of complex aerodynamic systems.
- Experience predicting, analyzing, and reducing tonal and broadband aerodynamic noise.
- Understanding of rotor-stator interactions, blade aerodynamics, duct acoustics, flow-induced noise, and propulsion-system integration.
- Experience connecting simulation with physical testing and experimental data.
- Strong programming, numerical modeling, and engineering-analysis capabilities.
- Interest in applying AI, machine learning, optimization, and generative engineering methods to aerospace design.
- Ability to operate independently and take technical ownership from initial concept through analysis, testing, and design iteration.
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