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PhD urban sound modelling

Job in 1000, Amsterdam, North Holland, Netherlands
Listing for: Koninklijk Nederlands Lucht- en Ruimtevaartcentrum
Full Time position
Listed on 2026-10-10
Job specializations:
  • Research/Development
    Data Scientist
  • Engineering
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 38000 - 47000 EUR Yearly EUR 38000.00 47000.00 YEAR
Job Description & How to Apply Below

Join a cutting-edge PhD in aircraft noise propagation modelling and help shape the future of quiet, sustainable flight!

Background

New aerospace concepts such as Urban Air Mobility (UAM) will introduce air taxis and delivery drones into densely populated urban environments. These technologies promise more sustainable and efficient transport while helping to reduce congestion. NLR and TU/e expect UAM operations to become a reality within the next decade. Unlike conventional aircraft, UAM vehicles are expected to operate at lower altitudes and closer to residential areas, potentially exposing many more people to aircraft noise in their daily living environment.

However, little is known about how people will experience these new sources of noise in complex urban settings. Current aircraft noise propagation models assume free-field sound propagation and therefore cannot adequately account for the influence of buildings and other urban structures on the perceived sound environment. New scientific research is therefore urgently needed to understand, predict, and ultimately mitigate the impact of UAM noise on people.

The aim of this research is to improve our understanding of the propagation UAM noise in urban environments, and to enable perception studies of UAM noise in cities via auralization. A key scientific challenge is to accurately model both sound propagation in urban environments. The outcomes of this research will help shape future noise assessment, mitigation strategies, and regulations for safe and socially acceptable UAM operations.

To address these challenges, you will develop and validate a next-generation noise propagation model that predicts the frequency-dependent sound field generated by Urban Air Mobility (UAM) vehicles in complex urban environments, using existing sound power characteristics of UAM vehicles. Unlike conventional aircraft noise models, the model will predict the full frequency-dependent magnitude and phase, enabling a realistic representation of the urban acoustic environment.

The model will generate both monaural sound levels for noise impact assessment and binaural audio signals for immersive virtual reality applications, such as NLR's Virtual Community Noise Simulator (VCNS). This will provide a unique platform for both quantitative noise assessment and perception studies, improving our understanding of how UAM noise propagates through cities and how it is experienced by people.

Validation is a crucial part of the research and will combine objective acoustic measurements. To this end, a measurement campaign will be carried out in a representative urban environment or at a controlled test facility, such as NLR's Digi City urban mock-up at the Mobility Innovation Test Centre (MITC). A perceptual validation study can be part of this research phase, depending on your expertise.

Finally, the validated model will be applied to practical use cases, demonstrating how it can support perception studies (e.g. using the VCNS) and inform the development of effective noise mitigation strategies for urban environments.

The project will start between September and December 2026.

Your profile
  • Master’s degree in (Building) Acoustics, Physics, Aerospace engineering or a related field;
  • Collaborative and self-directed,and receptive to constructive feedback;
  • Good communication skills in both spoken and written English, knowledge of Dutch is desirable but not mandatory;
  • Strong background in programming, using MATLAB and/or Python or other relevant programming languages;
  • Ability to learn independently and passion for research.
What we offer you

This position offers a unique combination of supervision from NLR and TU/e. NLR provides guidance from an applied research perspective, while…

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