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What you'll own:
Develop and validate source-noise prediction models for our novel lift and propulsion architecture, from first-principles aeroacoustics through empirically anchored corrections.
Design and execute acoustic measurement campaigns in anechoic facilities and at outdoor ranges, owning microphone array design, calibration chains, and data acquisition end to end.
Reduce rig and flight-test acoustic data into validated spectra, directivity maps, and component-level source rankings that directly drive vehicle design decisions.
Quantify community-noise exposure using EPNL, SEL, dBA, and tonality-corrected metrics, and translate the results into hard, testable requirements for design teams.
Build psychoacoustic models – loudness, sharpness, tonality, fluctuation strength – and run jury listening studies that connect physical metrics to human annoyance.
Run aeroacoustic simulations coupling CFD to acoustic analogy solvers (FW-H or equivalent), and reconcile predictions against measured data every design cycle.
Define and defend the vehicle noise budget at design reviews, allocating margins across subsystems and flagging acoustic risk before hardware is built.
Author measurement procedures, test reports, and model validation documents rigorous enough to anchor future certification arguments.
What you'll own:
Develop and validate source-noise prediction models for our novel lift and propulsion architecture, from first-principles aeroacoustics through empirically anchored corrections.
Design and execute acoustic measurement campaigns in anechoic facilities and at outdoor ranges, owning microphone array design, calibration chains, and data acquisition end to end.
Reduce rig and flight-test acoustic data into validated spectra, directivity maps, and component-level source rankings that directly drive vehicle design decisions.
Quantify community-noise exposure using EPNL, SEL, dBA, and tonality-corrected metrics, and translate the results into hard, testable requirements for design teams.
Build psychoacoustic models – loudness, sharpness, tonality, fluctuation strength – and run jury listening studies that connect physical metrics to human annoyance.
Run aeroacoustic simulations coupling CFD to acoustic analogy solvers (FW-H or equivalent), and reconcile predictions against measured data every design cycle.
Define and defend the vehicle noise budget at design reviews, allocating margins across subsystems and flagging acoustic risk before hardware is built.
Author measurement procedures, test reports, and model validation documents rigorous enough to anchor future certification arguments.
Requirements:
M.Sc. or Ph.D. in aerospace or mechanical engineering, acoustics, or physics, with a focus on aeroacoustics or noise control.
4+ years of hands-on aeroacoustics work – rotorcraft, propellers, fans, turbomachinery, or airframe noise – in industry or applied research.
Proven experience running acoustic test campaigns: microphone arrays, beamforming, anechoic or wind tunnel measurements, and full calibration traceability.
Strong acoustic signal processing skills – spectral analysis, order tracking, de-Dopplerization, coherence-based source separation – implemented in Python or MATLAB.
Working command of source-noise prediction methods (Ffowcs Williams-Hawkings, semi-empirical broadband models such as BPM, or equivalent) and a clear-eyed view of their limits.
Fluency in community and certification noise metrics – EPNL, SEL, dBA – and the measurement procedures behind ICAO Annex 16 / 14 CFR Part 36 style testing.
Demonstrated ability to own a question end to end: from test plan to instrumented rig to a conclusion you would defend in front of the whole company.
M.Sc. or Ph.D. in aerospace or mechanical engineering, acoustics, or physics, with a focus on aeroacoustics or noise control.
4+ years of hands-on aeroacoustics work – rotorcraft, propellers, fans, turbomachinery, or airframe noise – in industry or applied research.
Proven experience running acoustic test campaigns: microphone arrays, beamforming, anechoic or wind tunnel measurements, and full calibration traceability.
Strong acoustic signal processing skills – spectral analysis, order tracking, de-Dopplerization, coherence-based source separation – implemented in Python or MATLAB.
Working command of source-noise prediction methods (Ffowcs Williams-Hawkings, semi-empirical broadband models such as BPM, or equivalent) and a clear-eyed view of their limits.
Fluency in community and certification noise metrics – EPNL, SEL, dBA – and the measurement procedures behind ICAO Annex 16 / 14 CFR Part 36 style testing.
Demonstrated ability to own a question end to end: from test plan to instrumented rig to a conclusion you would defend in front of the whole company.
This position is open to all candidates.
