PhD Position in Underwater Acoustics Tallinn University of Technology in Estonia

Tallinn University of Technology (TalTech) School of Engineering, Department of Civil Engineering and Architecture, invites applications for a 4-year PhD position in Environmental, Coastal and Marine Technology, focusing on underwater acoustics. The project, supervised by Researcher Mirko Mustonen, is embedded in the Mechanics of Fluids and Structures research group and aims to develop and apply advanced methods for the detection and classification of underwater sounds, with a particular emphasis on the Baltic Sea.

The underwater environment is rich with diverse sounds, from natural phenomena like sea surface agitation and rain to biological signals such as gray seal claps and cod grunts, and increasingly, anthropogenic noise from merchant ships. The challenge lies in identifying and distinguishing these overlapping and subtle signals, which often require sophisticated analysis. This doctoral project will leverage signal processing and machine learning techniques to create algorithms capable of detecting and categorizing underwater sounds, initially using historic acoustic data from the Baltic Sea but with potential applications to other marine environments.

Key scientific motivations include improving the modeling and characterization of natural background noise to enhance the detectability of other sounds, and developing robust acoustic features beyond standard spectral representations to improve machine learning classification of biological, anthropogenic, and other sound sources. The research will involve assembling and labeling a comprehensive reference dataset, supported by weather data, wave models, AIS and VMS vessel tracking, and seismic survey information. Applications include building detectors for explosive events and ship noise, with the goal of identifying both compliant and non-broadcasting vessels and studying natural background sounds.

Feature design will be a major focus, testing autocorrelation-based descriptors, periodicity features, statistical moments, and alternative time–frequency transforms to optimize detection and classification accuracy. The final stage of the project will explore deploying these methods on autonomous underwater listening stations, emphasizing computational efficiency and real-time decision-making. The expected outcome is a generalizable, data-driven framework for underwater sound detection and categorization, applicable to the Baltic Sea and transferable to other marine environments.

The successful candidate will join a vibrant research environment, collaborating with underwater acoustics and electronics teams, and benefiting from access to acoustic sensors, underwater measurement platforms, and high-performance computing facilities. Opportunities include international research visits, joint experiments with industrial partners, training in signal processing, machine learning, and instrumentation, and presenting results at international conferences. Supervision will cover experimental design, numerical modeling, academic writing, and research management, with exposure to real-world problems and technology transfer through external collaborations.

Applicants should hold an MSc degree in data science, applied physics, applied mathematics, or a related discipline, and demonstrate strong interest in signal processing and machine learning. Essential skills include programming in Python, C++, and MATLAB, proficiency in English, and experience with experimental data for machine learning. Desirable backgrounds include machine learning and audio processing, wind-generated water wave modeling, underwater acoustics, and embedded machine learning. Motivation for interdisciplinary research involving experiments and field measurements is expected.

Application documents (CV, motivation letter, degree certificates, research plan, and passport copy) should be sent to [email protected]. Further information on PhD admission is available at TalTech's PhD admission page. Applications may also be submitted via Academic Positions. The application deadline is February 9, 2026.