冰下环境表征的被动声学监测

本项目研究冰下环境中的被动声学监测，利用声传播建模和机器学习表征环境状况并提升声呐性能。研究旨在了解冰产生的声音如何反映复杂北极环境的参数，从而增强海事感知能力。

About the project The project will investigate passive acoustic monitoring in under-ice environments, combining acoustic propagation modelling with machine learning and data analytics to characterise environmental conditions, improve understanding of ice-generated acoustic processes and their influence on sonar performance, and exploit this information to enhance under-ice situational awareness and maritime sensing capabilities. The Arctic and High North are becoming increasingly important operating environments, however the under-ice acoustic environment is a highly dynamic and complex physical system which remains difficult to observe, predict and exploit, and for sonar systems to operate reliably. Sea ice processes such as fracturing, ridging, deformation and movement influence the generation, propagation and scattering of sound and, consequently, sonar performance. Improved understanding of these system interactions is critical for sensing and situational awareness in under-ice environments. This project will investigate how passive acoustic monitoring can be used to characterise and understand the under-ice environment. Rather than relying solely on active sonar, the research will examine whether naturally occurring ice-generated sounds can provide exploitable information about environmental conditions. A particular focus will be placed on identifying, classifying and interpreting the acoustic signatures associated with physical ice processes and understanding their relationship to environment properties and acoustic behaviour. The project will combine physics-based acoustic propagation modelling, signal processing, statistical analysis and machine learning to develop methods for extracting environmental information from passive acoustic observations. By integrating physics-based and data-driven approaches the research aims to identify links between observed acoustic characteristics and key environmental parameters, enabling improved environmental characterisation where direct measurements are challenging or unavailable. The outcomes will improve understanding of under-ice acoustic environments and demonstrate how passive acoustic information can support environmental characterisation, sonar performance prediction and operational decision making. Potential applications include improved under-ice situational awareness and more effective use of maritime sensing systems in complex ice-covered regions.

Entry requirements You must have at least a UK 2:1 honours degree, or its international equivalent , in one of the following: physics mathematics engineering acoustics oceanography computer science a related discipline Desirable skills: quantitative background familiarity with scientific programming (Python, MATLAB or similar tools) familiarity with, or experience in, any combination of signal processing, machine learning, environmental modelling, acoustics, sonar, wave propagation, statistical analysis or numerical modelling experience working with environmental or sensor datasets interest in polar, marine or oceanographic environments

Funding: Competition funded Fees and funding For candidates looking to complete a PhD or EngD full-time we offer an enhanced tax-free stipend of up to £30k per year, along with a substantial budget for research, travel, and centre activities. CISDnS is committed to promoting equality, diversity and inclusivity. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break or are transitioning into a new role. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance.

How to apply Please apply via the online portal and select: Academic year: 2027/28 If you will be full time or part time Faculty: Engineering and Physical Sciences Search for "complex integrated systems" Select either PhD or EngD – if you are unsure, select PhD Add the name of the supervisor in section 2 of the application Applications should include: your CV (resumé) 2 academic references degree transcripts and certificates to date