Research Fellow (Urban Geophysics)

The School of Civil and Environmental Engineering (CEE) is a leading school for Sustainable Built Environment. Our mission in research is to achieve excellence by providing a conducive and intellectually stimulating environment to enable high quality work in strategic directions that are of significant impact to industry, science and technology. For more details, please view https://www.ntu.edu.sg/cee. We are looking for a Research Fellow, Urban Geophysics and Seismic Noise Monitoring to conduct research in urban and environmental geophysics using distributed acoustic sensing on pre-existing telecom fiber networks. The role will focus on ambient and anthropogenic seismic noise for urban subsurface characterization, seismic source characterization, geophysical imaging, and environmental monitoring including geohazard detection such as sinkhole and subsidence. Key Responsibilities: • Develop algorithms for passive seismology, seismic noise analysis, geophysical imaging, and seismic source characterization using DAS data from urban telecom fiber networks. • Design and conduct field experiments in dense urban environments using telecom fiber networks for subsurface characterization and environmental monitoring. • Analyze ambient and anthropogenic seismic noise to infer near-surface properties and detect geohazards such as sinkholes and subsidence. • Build scalable pipelines for processing, quality control, and interpretation of large DAS and seismic datasets. • Integrate distributed sensing observations with complementary geophysical and environmental data to improve urban subsurface imaging and monitoring. Job Requirements: • PhD in Geophysics, Seismology, Civil/Structural Engineering, Engineering Mechanics, Applied Physics, Electrical/Computer Engineering, or a related field. • Strong research record in distributed fiber-optic sensing, passive seismology, urban geophysics, seismic imaging, wave propagation, or near-surface characterization. • Experience processing large-scale DAS or seismic datasets, including signal processing, imaging, inversion, or source characterization, is highly desirable. • Proficiency in Python, C++, or Julia, with strong numerical modeling, data analysis, and reproducible code development skills; distributed or parallel computing is a plus. • Experience designing and executing field experiments in urban or environmental settings, with willingness to engage in fieldwork in dense urban environments. • Good written and oral communication skills, with the ability to publish in peer-reviewed venues and collaborate across interdisciplinary teams. • Experience with AI/ML for science and engineering, geohazard monitoring, or multimodal data fusion is advantageous. We regret to inform that only shortlisted candidates will be notified