Postdoc in Dynamic Noise Mapping

Job description Join KTH's MWL laboratory MWL | KTH to advance urban acoustic monitoring through research in dynamic noise mapping. You will develop real-time methods for road traffic noise assessment, including AI-based traffic classification and acoustic modeling of mixed vehicle fleets. A key focus will involve characterizing and modeling noise from emerging urban air mobility (UAM) operations, integrating UAV noise with road traffic for comprehensive urban soundscape assessment. Working within funded projects from Trafikverket and Digital Futures, you'll build upon previous projects in collaboration with the City of Stockholm on acoustic monitoring networks, which resulted in a growing testbed for acoustic-based traffic sensing. The position offers opportunities to collaborate with city planners, industry partners, and an international research network, while contributing to solutions for sustainable urban mobility. Your work will combine acoustic source characterization, propagation modeling, and real-time data processing to support evidence-based urban planning. You'll be part of a dynamic research group with strong ties to Swedish municipalities and access to unique datasets from Stockholm's urban environment. 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, noise modeling, signal processing, or data-driven methods Documented research experience through scientific publications Proficiency in English, both written and spoken As a person, you are curious, independent, and motivated to tackle complex interdisciplinary challenges at the intersection of acoustics, data science, and urban planning.You demonstrate: Research expertise in the aforementioned areas, and in line with the job description Teaching abilities Awareness of diversity and equal opportunity issues, with specific focus on gender equality Strong collaborative abilities (academic researchers, external partners from municipalities and industry) Independence Preferred qualifications A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with urban acoustic monitoring or transportation noise assessment Programming skills in Python Knowledge of machine learning techniques applied to acoustic or environmental data Experience with acoustic propagation models or sound source characterization Previous collaboration with non-academic stakeholders (municipalities, industry, government agencies) Great emphasis will be placed on personal skills