Postdoc in Acoustics-Based Traffic Sensing

Postdoc In Acoustics-Based Traffic Sensing

Join KTH's research in engineering acoustics at the Marcus Wallenberg Laboratory (MWL) and develop the next generation of acoustics-based traffic sensing. Cost-effective roadside microphones, combined with signal processing and machine learning, can provide continuous, real-time information on traffic flows, vehicle types and traffic noise, whereas cities today rely on short and intrusive measurement campaigns.

The position is part of two projects funded by Digital Futures, carried out together with the City of Stockholm, Nacka municipality and other municipalities in the region: a societal innovation project expanding an acoustic sensor network across the Stockholm region, and a proof-of-concept project developing a validated roadside monitoring system. You will build on established testbeds and unique datasets from Stockholm's urban environment.

You will deploy acoustic sensor units in urban environments, develop robust methods for detecting and classifying vehicle pass-bys, from processing on the sensor (edge) to the cloud, validate the results against reference measurements, and make data available through APIs and visualisation tools for municipal users. The position also includes scientific publishing, co-supervising students and contributing to new proposals and innovation activities.

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
• Strong background in at least two of the following areas: acoustic measurements, signal processing, machine learning, sensor systems or embedded systems.
• Documented research experience through scientific publications.
• Good programming skills, for example in Python.
• Proficiency in English, both written and spoken, as it is used in daily work.

As a person, you are curious, independent and motivated by research with practical impact. You demonstrate:

• Teaching abilities
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality
• Independence

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• Experience with acoustics-based traffic monitoring or with detection and classification of vehicles from sound.
• Experience deploying sensors or measurement systems in the field over long periods.
• Experience with machine learning for audio or time-series data, including deployment on edge devices.
• Experience with data pipelines, databases, APIs or web-based visualisation.
• Previous collaboration with non-academic stakeholders (municipalities, government agencies, industry).
• Interest in innovation and in the utilisation of research results.

Great emphasis will be placed on personal skills.

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Tagged as: Engineering, Physics