Here is a list of some of the funded research projects that I am involved in. I am always interested in recruiting excellent PhD students and Postdoctoral Research Associates through externally funded fellowship schemes and I am always happy to discuss the possibility of developing bespoke projects with potential students and fellows. If you are interested in reading for a PhD under my supervision, or working with me as research associate, please Get in touch.
DSOAP investigates how variability, coherence and uncertainty in the ocean environment affect ambient noise and underwater sound propagation. We will combine mathematical and physics-based modelling with statistical inference, signal processing and analysis of measured and simulated data to develop models, methods and software tools for maritime sensing, defence and national security.
Postdoctoral opportunities: Two full-time, two-year positions. View details and apply . Applications close on (reference 116953).
The project is based in the Department of Mathematical Sciences, with collaboration across Electrical Engineering and Electronics and engagement with Dstl, industry and government partners.
Inference of the ocean environment using measured and simulated acoustic data to train deep learning algorithms
This project uses machine learning to infer the ocean’s acoustic properties from measured and simulated sound data. Accurate descriptions of the marine environment are essential for effective sonar and for assessing the impact of marine renewable energy infrastructure on marine life, but direct measurements are difficult and expensive.
We will develop models that use acoustic data from in-situ and remote sensors, historical records and simulations to recover a broader range of ocean properties through acoustic inversion. The aim is to provide accurate, up-to-date descriptions of the acoustic environment, supporting sonar operation and responsible marine energy development.
Research team
Student: Joe Dobbins Primary supervisor:Dr Stewart Haslinger Secondary supervisor: Prof Daniel Colquitt Industrial Supervisor: Dr Duncan Williams (Dstl)
The project, undertaken in collaboration with the Dstl, is hosted at the University of Liverpool and is part of the Net Zero Maritime Energy Solutions CDT. The project will start on October 1st 2026 and runs for four years.
Met2Adapt: Modelling of the dynamic response for offshore wind farms
We will develop novel mathematical models to predict the dynamic phenomena arising in offshore wind farms and further model the acoustic waves generated by individual, and clusters of, wind turbines to help quantify the effect on wildlife, including birds, marine mammals, and fish.
Funded by the Marie Skłodowska-Curie Actions (Horizon Europe, Grant Agreement No. 101227175), Met2Adapt is a doctoral network for 16 PhD candidates who will be employed by one of the 10 partner institutions across Europe. Met2Adapt puts forward an ambitious research and training plan that will foster a new generation of researchers able to design and deliver sustainable meta-materials for vibration mitigation, self-aware meta-components and eventually carbon-efficient yet safe meta-structures for the renewable energy sector. The focal point of this research will be the deployment of custom-fit solutions for infrastructure that is critical to the European energy resilience, i.e. offshore and onshore wind farms, and wave-energy converters.
This work is funded by the EU under the Marie Skłodowska-Curie Actions (Horizon Europe, Grant Agreement No. 101227175).
BATNav: Bioinspired Acoustic Tactical Navigation
Our highly interdisciplinary research team is working on an innovative project aimed at enhancing first-person navigation in challenging environments using acoustics. The project focuses on developing an acoustic system that provides rapid identification of people, obstacles and topology (e.g. tunnels, stairways) in closed spaces, facilitating navigation for teams such as firefighters in complex, hazardous environments.
Whilst research in metamaterials (structured materials that can control waves and energy) has undergone a renaissance, the majority of work has focused on controlling a single physical domain - usually light. In contrast, this project seeks to develop a mathematical framework to study, design, and create multiphysical metasurfaces (Maradudin, 2011) capable of manipulating waves across many physical phenomena. Metasurfaces are structured interfaces where two or more physical systems interact, such as solids and fluids. Structured interfaces arise in many real-world applications - anywhere two media meet, from antenna designs, to the interface between the ocean and sea-bed, the foundations of modern buildings, medical implants, and safety-critical power generation components.
Research team
Student: Mr Jack Wildman Primary supervisor: Prof Daniel Colquitt Secondary supervisor: Dr Stewart Haslinger Post-doctoral Research Associate:Dr Katie Madine
Machine Learning for Data Driven Sound Propagation Modelling
This project will develop a series of high-fidelity digital twins capable of encapsulating a number of critical dynamic phenomena, which affect the propagation of sound waves through ocean environments, including internal waves, multi-scale structural thermal and temporal variations and fluctuations, scattering by non-smooth interfaces and boundaries (e.g. semi-submerged structures, sea bed, surface), currents, eddies, and fronts. The resulting sound propagation models, and advanced understanding embodied within these models, will enable the Royal Navy and other users of the ocean, the means to improve the effectiveness of their sonar systems and achieve the best results from sonar deployments and operations.
The developed models will be capable of intelligently adapting their approach and selecting the best solution method based on the input data, computational and operational constraints, desired outputs, and physical configuration. These models will incorporate advances in Finite Element Methods, via GPU parallel computing capability that has been largely untapped for underwater acoustics, along with hybrid semi-analytic coupling methods. Stochastic analysis of physical scattering models, incorporating real-world data, will provide a major step forward in the capability available to investigate dynamic ocean mechanisms. Analysis of simulated and measured oceanographic and acoustic data, including the use of artificial intelligence and machine learning techniques, will be supported by a parallel PhD project in the Department of Mathematical Sciences on Advances in mathematical modelling to study complex sound propagation in an inhomogeneous moving ocean. This will help to identify the properties and behaviour of different mechanisms. New ways of representing the specified mechanisms will be developed, and environment-specific modelling tools and innovative mathematical representations will be implemented.
Research team
Student: Mr Finley Boulton Primary supervisor: Prof Daniel Colquitt Secondary supervisor:Dr Sebastian Timme Industrial Supervisor: Dr Duncan Williams, Dstl
The project, undertaken in collaboration with the Dstl, is hosted at the University of Liverpool and is part of the Distributed Algorithms CDT and Signal Processing research community - a large, social and creative research group that works together solving tough research problems. The project started on October 1st 2023 and runs for four years.
Using machine learning and artificial intelligence to improve the tracking of vessels in sonar spectrograms
This PhD project explores creating an AI model that can correctly classify quiet targets in waterfall (sonar) data. Currently, waterfall data is analysed by human operators; however, this is time-consuming and expensive; these human operators outperform traditional automated passive contact follower algorithms, such as the Kalman and Alpha-Beta filters: these filters are susceptible to the abundant underwater noise and struggle with crossing tracks and quiet contacts. In contrast, humans can use their experience to learn how to mitigate the challenging aspects of the task. An automatic detection and tracking model that is more accurate and robust than traditional methods would reduce the human operator’s workload.
Research team
Student: Mr William Shaw Primary supervisor: Dr Murat Uney Secondary supervisor: Prof Daniel Colquitt Industrial Supervisor: Dr Cerys Jones, Ultra
The project, undertaken in collaboration with the Ultra Group, is hosted at the University of Liverpool and is part of the Distributed Algorithms CDT and Signal Processing research community - a large, social and creative research group that works together solving tough research problems. The project started on October 1st 2022 and runs for four years.
Underwater acoustics: Advances in mathematical modelling to study complex sound propagation in an inhomogeneous moving ocean
The aim of this well-funded PhD project is to develop models for underwater sound propagation through a moving inhomogeneous ocean, with an emphasis on enhancing the understanding of critical dynamic aspects, including:
Internal waves;
Microstructure variability
Scattering by rough surfaces, both stationary (seabed) and moving (sea surface); high sea states bring additional dynamic challenges such as large waves and infusion of air bubbles;
Seasonal, diurnal, and inter-diurnal changes;
Slow-moving horizontal currents, fronts, and eddies.
There is a lack of clarity within current literature as to the optimal representation of these dynamic phenomena within models, even for simplified two-dimensional cases. This project will address that issue by providing new insights that will also facilitate the development of three-dimensional and full four-dimensional, time-dependent propagation models.
Research team
Student: Miss Yiyi Whitchelo Primary supervisor: Dr Stewart Haslinger Secondary supervisor: Prof Daniel Colquitt Industrial Supervisor: Dr Duncan Williams, Dstl
A 4-year fully-funded EPSRC i-CASE studentship with the Defence Science and Technology Laboratory (Dstl) covering UK tuition fees, maintenance above the average UKRI Doctoral Stipend rate and an allowance for training, conference and research expenses.
Recently completed projects
Here is a list of some of the funded research projects that I have recently been involved in. If you are interested in the outputs or discussing potential follow-on projects or applications, please do get in touch.
MUSICA: Modelling UltraSonic Inspection of Challenging defects for Automated analysis
The project aimed to develop automated ultrasonic data analysis tools to improve the reliability of detection, sizing, and characterisation of defects which occur in high safety significance industrial plant. It focused on developing capability for defects of critical industrial importance, targeting two species: Thermal Fatigue (a service induced species) and Hydrogen cracking (a manufacturing/welding defect). Both species are known to occur and often materially impact plant availability/longevity. Its intended applications also included other challenging defect species, such as stress corrosion cracks.
Development of a modelling capability to simulate acoustic signal propagation through an oceanic environment, interactions of the signals with objects within the environment, and to predict the signals received by sensors.
We are supporting Sonardyne's work on Task 64 under the Defence Science and Technology Laboratory's Progeny Procurement Framework.
Task 79: Mathematics of Sensing
As part of the Dstl Future Sensing Programme, this Foundry project aimed to identify, understand, and assess novel mathematical approaches to a variety of problems in the Intelligence, Surveillance and Reconnaissance (ISR) domain.
