2026 PhD Projects

This is a PhD opportunity to work with Jaguar Land Rover on one of two PhD options (as listed below):

Required qualifications/skills:

• You should have or expect to get a 1st class honours degree or a Masters with distinction in a science or engineering subject.
• Experience of acoustic or vibration measurement, prediction and analysis is desirable.
• You should have a background in mechanical engineering, automotive engineering, electrical engineering, general engineering, physics or acoustics.

• Only available for UK or EU nationals
• Project Supervisors: Dr Joshua Meggitt and Dr Jon Hargreaves 
• Industry Partner: Siemens Industry Software NV 

Project Outline:
Predicting the noise and vibration from complex structures like cars is a common challenge in engineering design. Transfer Path Analysis (TPA) can be used for this NVH work, speeding prototyping and reducing design costs. In TPA, you break down systems into key components, coupling together models of vibration sources, such as electric motors, to passive receiver structures, like a car body. This PhD is about improving measurements that feed into TPA, to increase the accuracy of the noise and vibration predictions. Improving prediction accuracy, will help industry ensure their products meet noise regulations and increase vibro-acoustic comfort. 

Further project information:
You will develop approaches that guarantee accurate vibro-acoustic predictions with the least measurement effort. The first objective is exploring the high sensitivity of full-system predictions to measurement errors coming from individual components. The goal is to gain a more complete understanding of how different inconsistencies within a measured component-level frequency response function (FRF) matrix can lead to spurious peaks in the coupled FRF. You will work to create methods to automatically detect and remove spurious peaks via data quality checks on targeted component-level. A second objective is to research which degrees of freedom to include in the source identification using the blocked force method.

You will be working with Siemens, a leader in data acquisition and processing for TPA, and the Acoustics Research Centre at the University of Salford, which pioneered the block force measurement method enshrined in an ISO international standard.

Subject Areas: mechanical engineering, dynamics, acoustics, NVH, TPA, Transfer Path Analysis, vibroacoustics,

Required qualifications/skills:

• Only available for UK or EU nationals
• You should have or expect to get a 1^st class honours degree or a Masters with distinction in a science or engineering subject. 
• You must have some evidenced experience of acoustic or vibration measurement, prediction and analysis.

• Project Supervisors: Dr Simone Graetzer, Dr Sam Gregory, Prof. Jon Barker
• Project Partner: Royal National Institute of Blind People (RNIB)

Project Outline:
Blind and partially sighted people are heavily reliant on gathering information via aural means. However, having reduced visual input increases listening effort. You will either investigate the cocktail party effect for people with sight loss or reducing listening effort for screen readers at high word rates. 

Further project information:
You will explore one of the following projects:

1. Cocktail party effect for blind and partially sighted people
   Being able to follow a group conversation in a noisy environment is important in restaurants, pubs and other social situations. When participants have reduced visual cues, this makes turn-taking and picking out the speech-in-noise harder. You will start with researching how conversations work in such situations. You will then explore and develop technology that aids those with reduced sight. Inspiration may come from technologies used by people with hearing loss.
   
2. Reducing listening effort for screen readers at high word rates
   Screen readers are an important assistive technology for people with sight loss. Screen readers convert content presented visually into speech. Often high word rates are used to increase the speed at which information is communicated. However, this is mentally tiring.

You will first use biophysical measurement and self-report to quantify and evidence the listening effort problem and identify different user profiles. Then you will explore how to reduce listening effort through (1) optimising the speech synthesis and (2) the use of Large Language Models to optimise information delivery.

Subject Areas: Physics, Computer Science, AI, Machine Learning, Human Computer Interaction, Acoustic Engineering, Electronic Engineering, Linguistics, Mathematics, Speech Science, Acoustics, Psychology

Previous knowledge required:

• You should have or expected to again an Honours or Master’s degree in Acoustics, Engineering, Physics, Psychology, Computer Science, Neuroscience or a related field.
• Skills in programming will be required.
• Some familiarity with machine learning, assistive technology or speech science is desirable. 

• Project Supervisors: Dr Simone Graetzer and Prof Stefan Bleeck
• Industry Partner: Sonos

Project Outline:
This project aims to create a new generation of personalised headphone audio technology that adapts intelligently to an individual’s unique hearing profile and listener preferences. All users, whether they have hearing loss or not, want an effortless, high-fidelity listening experience.
You will be working closely with Sonos and using the latest technology, and acoustics facilities at the University of Salford, to develop a set of psychoacoustic measures for understanding users better and improving audio processing to enhance the listening experience.

Further project information:
Moving beyond the static presets seen often in consumer products, this project will develop a sophisticated framework for modelling hearing and listener preference to deliver personalised headphone audio. The goal is to deliver a perfectly balanced sound output, optimised for preference such as speech intelligibility, music enjoyment, or spatial immersion. The research will specifically address complex auditory characteristics and diverse listening needs such as hearing loss, loudness recruitment and deficits in temporal processing.

The core objective is to create predictive, individual models of the users’ hearing and listener preferences. This will be achieved by developing a suite of efficient psychoacoustic measures that go beyond simple auditory thresholds. These tests will probe spatial audio perception, loudness, temporal acuity, and speech-in-noise abilities. We will employ a “user-in-the-loop” optimisation strategy, where interactive tests gather rich data while simultaneously engaging the user in their own auditory discovery process.

The data will be used to train a personalised computational model that can predict the preferred audio processing strategies for that specific listener, given the listening context. The resulting low-latency model will be able to do real-time adaptive processing on the device to enhance the listening experience. The results will also be useful to inform the user about potential concerns about their hearing health.

Subject Areas: Acoustics, Acoustical Engineering, Electronic Engineering, Mathematics, Machine Learning, Artificial Intelligence, Physics

Required qualifications/skills:

• You are must have or expected to achieve a first-class or 2:1 Honours degree or Master’s degree in Acoustics, Engineering, Physics, or a related field
• Experience with acoustics and psychoacoustics, audio digital signal processing, mathematics, and some understanding of human hearing. Strong analytical and quantitative research skills.
• Excellent written and verbal communication skills, with the ability to present complex information clearly and concisely. 
• Also relevant are Machine Learning and optimisation algorithms, user research and software engineering

• Project Supervisors: Dr Joshua Meggit and Dr Levingshan Augusthus Nelson 
• Industry Partner: Farrat
  
• The project partner is offering a widening participation stipend top-up of £10,000 for UK home fees applicants from underrepresented groups.

Project Outline:
This PhD project tackles one of the most pressing challenges in modern construction: how to optimize vibration isolation systems to minimize concrete usage, reduce environmental impact, and enhance urban living. By combining advanced Finite Element modelling, laboratory and field testing, and collaboration with industry leader, Farrat Isolevel, the research will deliver practical design recommendations and performance prediction tools. The outcomes will directly support net-zero goals, improve health and well-being by reducing noise and vibration, and set new standards for sustainable building design.

Further project information:
The research will utilize advanced Finite Element modelling to simulate a range of building vibration isolation scenarios, complemented by laboratory and field testing to ensure robust validation of these models. Key areas of investigation will include the influence of soil and ground pressure, as well as the optimization of structural components such as columns and cores, with the aim of enhancing both performance and material efficiency in vibration isolation systems. Ultimately, the project seeks to deliver practical, evidence-based design recommendations and predictive tools that can be directly applied to real-world building projects.

Subject Areas: Vibration isolation, Structural dynamics, Finite Element Analysis (FEA), Sustainable construction, Building acoustics and noise control, Urban design and net-zero construction, Experimental testing and validation.

Required qualifications/skills:

• You are must have or expected to achieve a first-class or 2:1 Honours degree or Master’s degree in civil or structural engineering.
• A strong background and keen interest in structural dynamics and vibration.
• Hands-on experience in measurements, experimentation, and Finite Element modelling techniques.
• A commitment to advancing net-zero goals and enhancing quality of life in urban environments through innovative construction practices is essential.

• Project Supervisors: Prof. Antonio J Torija Martinez and Dr Simone Graetzer
• Industry Partners: UKHSA

Project Outline:
This PhD project explores how urban green and other public amenity spaces can be designed as restorative sound environments that protect the local population from the adverse health effects of transport noise and offer opportunities for enhancing health and well-being. While green spaces are known to mitigate noise-related impacts, the role of soundscape quality—what people actually perceive and how it affects them—remains poorly understood. This research takes a novel, person-centred approach to examine how natural and urban sounds influence perceived and actual restorative benefits across diverse communities. By integrating acoustic data, human perception and health outcomes, the project will develop an evidence-based assessment tool to guide the design and planning of urban green spaces that are acoustically restorative. Such a tool would inform policy and infrastructure decisions that support healthier, more equitable, and more resilient cities.

Further project information:
Good-quality green spaces benefit health and wellbeing. However, what makes a green space “good quality” is often not clearly defined.

This research will focus on the restorative potential of sound environment in urban shared open amenity spaces.

Some population subgroups may benefit especially from well-designed green spaces. These include disadvantaged communities facing life challenges and individuals living with mental health conditions. Inequalities are known to exist in noise exposure and access to green spaces, and some green space designs may not deliver the expected restorative benefits.

This project will examine how different sounds within and around urban amenity spaces affect perceived and actual health benefits. The hypotheses are: 1) Soundscape dominated by natural sounds are better perceived and more likely to lead to restorative effects; 2) Access to green spaces protected from transport noise and featuring positive sources provide significant restorative health benefits; and 3) Different population subgroups experience differing extent of restorative benefit from green spaces.

Through a person-centred approach and user-based perceptions, the research will identify targeted interventions that reflect local needs and experiences.

The findings will help tailor investments in restorative urban green spaces from an economic, social and environmental perspective. This can help create healthier, more resilient and more equitable communities.

Subject Areas: Acoustics, psychoacoustics, social sciences, public health, health and medical sciences, epidemiology.

Required qualifications/skills:

You must have or expected to achieve a BSc (First) or MSc (Merit) in acoustics, urban planning, psychology, social sciences, public health or environmental health.

• Project Supervisors: Prof Mahdi Azarpeyvand , Dr Mohammad Jadidi and Dr Daniele Zagaglia
• Industry Partner: Safran SLS

Project Outline:
Aircraft noise remains a major environmental and public health concern, with landing gear systems representing a significant contributor to the overall noise signature during approach and landing. Understanding the complex mechanisms behind landing gear noise generation requires detailed knowledge of the turbulent flow development around these intricate geometries.

In this exciting PhD project, conducted in close collaboration with Safran SLS, we aim to advance the fundamental understanding of noise generation in realistic landing gear systems. The project will combine experimental and analytical approaches to investigate the aerodynamic and aeroacoustics behaviour of realistic landing gear configurations.

The extensive datasets obtained from these experiments will support the application of AI and Machine Learning techniques for the development of fast and reliable noise prediction tools. This project offers an excellent opportunity to combine hands-on experimental work with advanced data-driven and AI-based modelling approaches, providing a holistic training in modern aeroacoustics research.

The successful candidate will have access to the National Aeroacoustics Wind Tunnel and state-of-the-art measurement facilities, including high-speed PIV systems, hot-wire anemometry, and beamforming microphone arrays. The project also offers collaboration with industry experts, a placement at Safran, and opportunities to present research findings at leading international conferences.

Further project information:

This PhD project will experimentally investigate the aerodynamic and aeroacoustics characteristics of landing gear systems using advanced measurement techniques and data-driven analysis. The focus will be on identifying the dominant flow structures responsible for noise generation and understanding their interactions with complex geometrical features. Key objectives include:

• Characterise the turbulent flow field and associated acoustic signatures across different landing gear configurations.
• Apply modal decomposition and statistical analysis (e.g., POD, SPOD) to identify dominant noise-generation mechanisms.
• Generate high-fidelity benchmark datasets for validation of computational and AI-based aeroacoustic prediction models.
• Support the design and assessment of next-generation low-noise landing gear concepts.

Working closely with Safran SLS and the University of Bristol’s aeroacoustics research group, the successful candidate will gain experience in experimental fluid dynamics, aeroacoustics, and data-driven and reduced order modelling.

Subject Areas: Fluid Dynamics, Aeroacoustics, Turbulence, Experimental Methods

Required qualifications/skills:

• Essential: 
  • Minimum 2:1 (or equivalent) in Aerospace, Mechanical Engineering, Physics, Mathematics, or related discipline. 
• Desirable: 
  • Interest in aerodynamics, aeroacoustics and flow-induced noise. 
  • Experience with wind tunnel testing, PIV, or microphone array measurements. 

• Project Supervisors: Dr Daniel Poole, Dr Mohammad Jadidi and Prof Mahdi Azarpeyvand
• Industry Partner: Safran SLS

Project Outline:
The noise generated by complex aircraft components, such as landing gear systems, presents a significant scientific and engineering challenge. Reducing this noise is crucial as aircraft manufacturers strive to meet stringent environmental regulations and minimise the acoustic footprint of modern aircraft. Accurately predicting noise from such complex geometries depends on a detailed understanding of the underlying unsteady flow development around these bodies.

In this exciting PhD project, conducted in close collaboration with Safran SLS, we aim to use state-of-the-art computational tools, including the GPU supported Lattice Boltzmann Method (LBM), to characterise the flow field around representative landing gear configurations and uncover the mechanisms responsible for noise generation. The research will combine high-fidelity CFD and Computational Aeroacoustics (CAA) with advanced data-driven analysis techniques such as Proper Orthogonal Decomposition (POD), to identify dominant turbulent structures and their acoustic significance.

The large, high-resolution numerical datasets generated will also be leveraged for Machine Learning (ML) applications, enabling the development of fast and accurate noise prediction and flow reconstruction tools for complex geometries. The outcomes of this work will directly support the design of next-generation low-noise landing gear systems, contributing to quieter and more sustainable aircraft.

The successful candidate will have access to the ProLB (LBM solver) and High-Performance Computing (HPC) facilities at the University of Bristol (UoB), as well as opportunities for experimental validation using the National Wind Tunnel facilities at UoB. The project offers close collaboration with Safran’s world-leading R&D team, including an industrial placement, and opportunities to present research findings at major international conferences.

Further project information:
This PhD project will explore the fundamental fluid–acoustic mechanisms responsible for noise generation by landing gear configurations using advanced numerical simulations and data-driven analysis. The research will employ high-fidelity CFD tools to resolve the fine-scale turbulent structures and their interactions with the gear geometry. CAA modelling will be used to propagate and analyse the radiated sound field.

Beyond traditional CFD, the project will leverage modal decomposition techniques (e.g., POD) to extract coherent flow features and identify dominant noise-producing mechanisms. The resulting numerical datasets will also be exploited for Machine Learning applications, enabling the development of surrogate models for rapid flow and noise prediction. These tools will help accelerate acoustic design optimisation and provide deeper physical insight into the coupling between flow dynamics and noise generation.

The research will advance knowledge in several important areas, including:

• Determination of the types of noise being generated (broadband, tonal, or mixed).
• Identification of the locations and strengths of dominant noise sources.
• Establishment of best practices for numerical, spatial, and temporal resolution to capture noise-producing flow features.
• Development of guidelines for effective noise-reduction technologies in future civil transport aircraft.

Working in close collaboration with Safran SLS and researchers at the University of Bristol, you will have access to advanced computational resources and industrial expertise. The project also offers opportunities to engage with experimental and modelling partners, ensuring that outcomes are both scientifically rigorous and directly relevant to real-world aircraft design.

Subject Areas: Fluid Dynamics; Turbulence; Experimental Aerodynamics; Aeroacoustics

Required qualifications/skills:

• Essential
  • Minimum 2:1 (or equivalent) in Aerospace, Mechanical Engineering, Physics, Mathematics, or related discipline.
  • Programming experience in Python or MATLAB.
• Desirable:
  • Interest in aerodynamics and/or aeroacoustics

• Project Supervisors: Prof Mahdi Azarpeyvand, Dr Esmaeel Masoudi and Dr Sen Wang
• Industry Partner: ESDU – ACCURIS 

Project Outline:
As urban air mobility continues to develop, electric vertical take-off and landing vehicles (eVTOLs), drones, and air taxis are expected to transform the way people and goods move within and between cities. These emerging technologies promise cleaner, more efficient transport, but their success depends on achieving low noise levels that make them acceptable to the public and compatible with urban environments. Reducing noise is therefore a key challenge for creating sustainable and widely adopted air-mobility systems.

This PhD project investigates the mechanisms behind broadband noise generation in propellers and rotorcraft systems. In collaboration with ESDU, an international leader in providing validated aerospace engineering design data, methods and software, and world-class academics at the University of Bristol, you will use the state-of-the-art National Aeroacoustic wind tunnel facility to study the steady or unsteady aerodynamics responsible for broadband noise generation. By combining advanced experiments with numerical simulations, the project aims to develop predictive models and practical strategies for reducing noise in next-generation aircraft propulsion systems.

Further project information:
Broadband noise generated by propellers and rotorcraft remains a major challenge for urban air mobility and next-generation aircraft. Unlike tonal noise, which is concentrated at specific frequencies, broadband noise spans a wide range of frequencies and arises from complex interactions between turbulent flows, blade loading, and wake dynamics. Understanding these mechanisms is essential for designing quieter, more efficient, and publicly acceptable aircraft.
In this PhD, you will investigate the aerodynamic processes that produce broadband noise in propellers and rotor systems. The research will combine wind tunnel experiments at the National Aeroacoustic Wind Tunnel Facility at the University of Bristol with numerical simulations to capture the steady or unsteady flow features responsible for noise generation. The experimental work will include measurements of flow and acoustic fields around rotating blades, while the simulations will provide detailed insight into the underlying aerodynamic mechanisms.

You will also explore ways to integrate experimental data and simulation results into predictive models, helping to identify design strategies for noise reduction. This may involve surrogate modelling, reduced-order modelling, or data-driven approaches.

The project is conducted in collaboration with ESDU, a leader in providing validated aerospace engineering design data, methods and software, providing direct access to industrial expertise and real-world design challenges. You will gain experience in state-of-the-art experimental and computational methods, contribute to the development of low-noise rotorcraft technologies, and play a part in shaping the future of urban air mobility and eVTOL systems.

Subject Areas: Fluid Dynamics, Aeroacoustics, Turbulence, Experimental Methods

Required qualifications/skills:

• A minimum of a 2:1 undergraduate degree in Aerospace Engineering, Mechanical Engineering, Physics, Mathematics or a related discipline.
• Strong background in fluid mechanics, aerodynamics, or acoustics.
• Experience with experimental testing, flow measurement, or computational modelling is desirable.

• Project Supervisors: Dr Daniele Zagaglia, Dr Djamel Rezgui,  and Prof Mahdi Azarpeyvand 
• Industry Partner: Leonardo Helicopters

Project Outline:
This PhD project will investigate the aerodynamic and aeroacoustic behavior of one of the most promising future aircraft architectures: the tiltrotor. In collaboration with Leonardo Helicopters, a global leader in civil rotorcraft manufacturing, you will explore the complex aeroacoustic and aerodynamic mechanisms at play in this configuration through experimental studies and, potentially, numerical simulations representing various flight conditions. The outcomes of this research will contribute to the development of quieter, more efficient aircraft, supporting the future of sustainable global aviation.

Further project information:
Tiltrotor aircraft will play a unique and increasingly important role in aviation due to their ability to combine the vertical lift capabilities of helicopters with the speed and range of fixed-wing airplanes.

In this PhD project, you will identify noise sources arising from complex flow interactions between the rotor, nacelle, wing and fuselage in tiltrotor aircraft configurations. You will design and conduct experiments and potentially numerical simulations, producing extensive aerodynamic and aeroacoustic datasets for various flight conditions to further the understanding of the noise generation mechanisms in tiltrotors and assess their performance.

The project provides access to state-of-the-art Aeroacoustic and computational facilities at the University of Bristol and offers the opportunity to work within a large, collaborative and inclusive research community. You will benefit from close collaboration with Leonardo Helicopters, one of the world’s top civil helicopter manufacturers, and gain valuable experience working alongside several industry and academic experts. Depending on the priorities and preferences of the industrial sponsor, you may also engage in short visits or undertake a 3–6 month secondment to work closely with the industry partner.

This is a very exciting opportunity to contribute to world-class research that will help developing the future of aviation. The work you do will have direct impact in enabling smarter, quieter, and more sustainable designs for the future of global transportation.

Subject Areas: Aeroacoustics, Aerodynamics, Experimental Methods, Experimental engineering, Computational Fluid Dynamics (CFD)

Required qualifications/skills:

• A minimum of a 2:1 undergraduate degree in Aerospace Engineering, Mechanical Engineering, Physics or Mathematics or a related discipline.
• Familiarity with experimental techniques (e.g., load measurement, steady and unsteady pressure measurements, Particle Image Velocimetry, etc) and/or CFD is desirable.
• Familiarity with data analysis is desirable.

• Project Supervisors: Dr Esmaeel Masoudi, Dr Claudia Nicolai and Prof Mahdi Azarpeyvand
• Industry Partner: ONERA, The French Aerospace Lab 

Project Outline:
This PhD project takes on one of the most fascinating challenges in modern aerospace engineering: understanding how turbulent airflows create noise and vibration when they interact with aircraft surfaces. These turbulent pressure fluctuations are key to designing the next generation aircraft, ship and other transport systems.

Working with leading researchers at the University of Bristol and the French Aerospace Lab (ONERA), you will combine advanced experimental techniques, numerical simulations, and machine learning to study turbulent flow behaviour and its impact on noise generation. The outcome of this research will support the design of quieter, more efficient, and environmentally responsible aerospace and marine transport systems.

Further project information:

The interaction between turbulent airflows and solid surfaces lies at the heart of many challenges in aerospace and mechanical engineering. When air moves over an aircraft wing, or fuselage, it generates small but strong pressure fluctuations that can cause noise and vibration. These effects become increasingly important as new aircraft designs aim for higher efficiency, lighter structures, and lower environmental impact.

In this exciting PhD, you will explore how these flow-induced pressure fluctuations develop and how they can be better predicted. You will join an international research collaboration between the University of Bristol and the French Aerospace Lab (ONERA), bringing together world-leading expertise in aeroacoustics, fluid dynamics, and machine learning. Through a combination of advanced experimental techniques, numeral simulations, and artificial intelligence (AI), you will explore how turbulent flows generate pressure fluctuations and develop new predictive tools for noise and vibration.

The project provides access to state-of-the-art boundary layer wind tunnel facilities at the University of Bristol and offers the opportunity to work within a large, collaborative research community. You will benefit from collaboration with ONERA, one of Europe’s top aerospace research organisations, and gain valuable experience working alongside several other industry and academic partners.

This is an exciting opportunity to be part of world-class research that supports the development of innovative aircraft configurations, wide-body transport systems, and marine technologies. The work you do will contribute directly to creating smarter, quieter, and more sustainable designs for the future of global transportation.

Subject areas: Fluid Dynamics, Turbulence, Aeroacoustics, Experimental Methods, Machine Learning

Required qualifications/skills:

• A minimum of a 2:1 undergraduate degree in Aerospace Engineering, Mechanical Engineering, Physics or Mathematics or a related discipline

• Project Supervisors: Prof Mahdi Azarpeyvand, Dr Esmaeel Masoudi and Dr Sen Wang 

Project Outline:
As electric vertical take-off and landing (eVTOL) aircraft move closer to widespread adoption in future cities, the design of vertiports which are dedicated take-off and landing hubs, has become increasingly important in ensuring safe, efficient, and publicly acceptable urban air mobility systems. Noise is one of the key challenges facing this transition. The placement, design, and operation of vertiports will determine how acceptable eVTOL systems are to the communities they serve, particularly in dense urban and peri-urban areas.

This PhD project will explore how vertiport layout, rotorcraft operations, and atmospheric conditions influence noise generation and propagation. Through a combination of experimental studies, aeroacoustic modelling, and urban planning analysis, the project aims to develop design guidelines and regulatory insights for quieter, safer, and more sustainable vertiport integration into future cities.

Further project information:
Urban air mobility promises to revolutionise the way people move within and between cities, but achieving this vision requires careful consideration of noise, safety, and environmental impact. Vertiports will form the backbone of these networks, serving as take-off and landing sites for fleets of electric air taxis and drones. The vertiport design will strongly influence local noise levels, public acceptance, and urban integration.

This PhD will investigate the aeroacoustic and environmental aspects of vertiport design, with a particular focus on the ground effect and atmospheric boundary layer interactions that affect rotor noise generation. Using experimental testing of single- and multi-rotor configurations, the research will measure detailed noise signatures, flow behaviour, and surface interactions during take-off and landing. These data will be used to evaluate the influence of vertiport geometry, surface materials, and environmental conditions on perceived noise levels.

You will work within the University of Bristol’s National Aeroacoustic Facility and collaborate with multiple academics and industrial partners in aeroacoustics, fluid dynamics, and urban design. The project may also involve engagement with industry and policy partners involved in eVTOL development and city planning. Through this research, you will contribute to defining the standards and practices that will shape the integration of air mobility into modern cities.

Subject Areas: Urban Air Mobility (UAM); Aeroacoustics and Environmental Noise; Experimental Fluid Mechanics; Urban Design and Transport Planning; Sustainable Aviation

Required qualifications/skills:

• A minimum of a 2:1 undergraduate degree in Aerospace Engineering, Mechanical Engineering, Civil Engineering, Physics, or a related discipline.

Project Supervisors: Prof Mahdi Azarpeyvand, Dr Mohammad Jadidi and Dr Esmaeel Masoudi

Project Outline:
Aircraft noise remains a major environmental challenge as the aviation industry moves toward quieter and more sustainable propulsion systems. Low-frequency noise, produced by jet engines and fans, is particularly difficult to suppress using conventional acoustic liners due to its long wavelengths and high energy content.

This PhD project will explore the use of acoustic metamaterials to achieve effective low-frequency noise reduction in aircraft systems. Working with world-class academics at the University of Bristol and several industry and academic partners, you will investigate novel liner concepts designed to suppress low-frequency broadband noise. The project aims to develop compact, high-performance acoustic metamaterials that can significantly reduce aircraft noise footprints and support the next generation of quiet, efficient, and environmentally friendly air transport.

Further project information:
Acoustic liners are specially designed surfaces used in aircraft engines and propulsion systems to absorb sound energy and reduce the noise that reaches the environment. Traditional liners often struggle to control low-frequency noise because the long sound wavelengths are difficult to absorb within the limited space available in modern aircraft structures.

Low-frequency metamaterials represent an emerging frontier in aeroacoustics, offering new possibilities for noise reduction beyond the limits of conventional passive treatments. In contrast to traditional liners, metamaterials can achieve strong acoustic control through subwavelength resonant mechanisms, that enable efficient sound absorption at frequencies previously difficult to reach. By integrating elements such as Helmholtz resonators, membrane absorbers, and labyrinthine channels, these materials can be engineered to exhibit locally resonant behaviour, that allows precise tuning of their acoustic response.

The PhD candidate will conduct experiments using the state-of-the-art Grazing Flow Impedance Tube at the University of Bristol, that enables detailed characterisation of liner performance under realistic flow and acoustic conditions. The work will also make use of advanced measurement tools, including high-speed Particle Image Velocimetry (PIV) and hot-wire anemometry, to capture detailed flow–acoustic interactions.

The project involves close collaboration with industry partners and offers placements with both academic and industrial collaborators, providing a strong platform for research impact and professional development. The student will also be encouraged to participate in international training schools and conferences, presenting their findings to the wider aeroacoustics and metamaterials research community.

Subject Areas: Acoustic Metamaterial; Fluid Dynamics; Experimental Methods; Aeroacoustics; Noise Control; Sustainable Aviation

Required qualifications/skills:

• A minimum of a 2:1 undergraduate degree in Aerospace Engineering, Mechanical Engineering, Civil Engineering, Physics, or a related discipline.

• Project Supervisors: Dr Anton Krynkin and Dr Melika Gul
• Industry Partner: TNEI Group 

Project Outline:
This project will tackle wind turbine Amplitude Modulation (AM), one of the critical barriers to onshore renewable energy acceptance. Our physics-informed machine learning model will provide the first reliable AM prediction capability, addressing a significant industry need. This enables proactive mitigation, reducing noise complaints and fostering public trust. The data driven part of the model will leverage the existing datasets, that will enable us to build a robust, predictive tool where current methods fall short. The project is interdisciplinary and combines acoustics, aerodynamics, and machine learning disciplines.

Further project information:
Wind turbine amplitude modulation (AM) is referred to “periodic fluctuations in the level of audible noise from a wind turbine” with “the frequency of the fluctuations being related to the blade passing frequency of the turbine rotor” [1]. The audible effect of AM is usually described with ‘‘whoomph’ sound and can sometimes be observed at residential distances from a turbine (or turbines). This differs from well-defined wind turbine noise, often described as blade “swish” when in close proximity to turbines, which is an inherent property of wind turbines as noise is radiated from the trailing edge of the blades as they rotate. It has been reported that AM is widely linked to noise complaints, and the AM phenomenon is mentioned in the guidance on Assessment and Rating of Noise from Wind Farms [2]. The complexity of the phenomenon was associated with the environmental conditions, wind turbine dependency and presence of reflecting surfaces [2].

In this project it is proposed to build a physics-based machine learning model that will account for aerodynamic sound sources, sound propagation in atmosphere and effect of the reflection from the ground. We will investigate and quantify the critical role of turbine blade geometry and blade position with respect to the incoming flow and nacelle, airflow conditions (windspeed and direction, free stream turbulence and shear gradient), wake-turbine interactions. This model will inform understanding of the phenomenon and will include stochastic algorithms such as Markov Chain Monte Carlo enabling inclusion of uncertainty analysis and recovery of the AM onset and its rating [1] as probability distribution.

The results of this project are expected to contribute to the efforts of building a robust approach for measuring and assessing AM. It has the potential to advance planning conditions essential for the successful installation of onshore wind turbines and enhance the management of limiting and controlling noise associated issues [1].

The project will be supervised by a cross-disciplinary team that will include academic expertise in acoustics (Dr Anton Krynkin) and aerodynamics (Dr Melika Gul) as well as the industry expertise offered by TNEI Group, Newcastle.

[1] IOA Noise Working Group, Final Report on A Method for Rating Amplitude Modulation in Wind Turbine Noise, Institute of Acoustics, Version 1, August 2016

[2] Noise Working Group, Final Report on Assessment and rating of noise from wind farms, ETSU-R-97, September 1996.

Subject areas: Wind turbines, amplitude modulation, aerodynamic noise, machine learning, stochastic algorithms, uncertainties

Required qualifications/skills:

• A minimum of 2.1 or above Bachelor or Master degree in Engineering, Physics, Mathematics or other related areas.  
• Experience in acoustics, vibration and numerical simulation with finite element simulation or computational fluid dynamics methods will be desirable. 

• Project Supervisors: Prof Jon Barker and Dr Anton Krynkin
• Industry Partner: NHS (and TinnitusUK) 

Project Outline:
Over seven million people in the UK live with tinnitus — the persistent perception of sound when no external source is present. Tinnitus can cause distress, anxiety, and loss of concentration, and current therapies are often costly, complex, or inaccessible. This project aims to change that by combining machine learning, clinical expertise, and acoustic subwavelength structures (a.k.a. metamaterials) and additive manufacturing to create an affordable, personalised therapy that requires no electronics or batteries. The outcome will be a sustainable, accessible treatment—one that uses sound science to improve lives through innovative, low-cost, and environmentally friendly technology.

Further project information:
Notch noise therapy is a promising treatment for tinnitus – the perception of sound in the absence of an external source. Instead of masking tinnitus with more noise, this approach removes a narrow “notch” in the frequency band matching the patient’s tinnitus pitch. Over time, this helps to retrain auditory pathways in the brain, reducing the perceived loudness and distress caused by tinnitus. However, existing implementations rely on electronic devices that are costly and inaccessible for many sufferers.

This PhD project aims to make tinnitus notch noise therapy widely accessible through a combination of machine learning, clinical data modelling, and 3D-printed acoustic metamaterials. The central research question is:
How can personalised tinnitus therapy be delivered effectively using passive acoustic devices designed from perceptual and machine learning models?

The project has two main stages:

1. Tinnitus Profiling and Modelling – Collaborate with tinnitus patients to map perceptual profiles through sound matching and subjective descriptions. Develop machine learning models and explore large language models (LLMs) to infer tinnitus parameters from natural language descriptions and NHS data.
   
2. Passive Therapy Design – Use additive manufacturing technology to design and prototype acoustic metamaterial filters tuned to each patient’s tinnitus profile, enabling notch-filtering therapy without electronics.

The project will be co-supervised by experts from Computer Science, Mechanical Engineering, and Sheffield Teaching Hospitals, ensuring a strong link between clinical application and technical innovation.

Sheffield Teaching Hospitals will provide access to clinical expertise and patient cohorts, ensuring that the research is grounded in real clinical need and focused on outcomes that can translate directly into NHS practice. Their involvement will also help guide validation studies and inform regulatory pathways for future deployment.

Key references:

• Mizukoshi, F. et al. (2023). Biomedical Engineering Advances, 6, 100102.
• Mizukoshi, F. & Takahashi, H. (2021). PLOS ONE, 16(10), e0258842.
• Tong, Z. et al. (2023) Ear Hear 44(4):670-681

Subject Areas: Tinnitus Therapies; Machine-Learning; Large Language Models; 3-D Printing; Acoustic Metamaterials;  Computational Audiology; Human Perception; Health Technology; Clinical Data Science 

Required qualifications/skills:

1. A good undergraduate or master’s degree (2:1 or above, or equivalent) in Computer Science, Engineering, Acoustics, Physics, or a closely related field.
   
2. Experience in one or more of the following areas:
   • Machine learning, signal processing, or data modelling
   • Audio/acoustic analysis or human perception studies
   • 3D design and additive manufacturing
   • Human-computer interaction or app development (desirable)
     
3. An interest in applying computational and acoustic research to real-world clinical problems.

• Project Supervisors: Prof Paul Kemp and Prof Paul White
• Industry Partner: Environment Agency

Project Outline:
The European eel is listed as “Critically Endangered”, experiencing population declines of over 90%. One stressor is a non-native parasitic nematode, introduced by Japanese eel imported for the restaurant trade, that damages the swim bladder, an organ critical to buoyancy. This likely impacts eel during their 5000 km spawning migration from European rivers to the Sargasso Sea. Monitoring infection is a major conservation challenge as an autopsy is required to confirm parasite presence. This destructive technique conflicts with conservation goals due to the need to kill the eel, and as such means the number of surveys and eels sampled are kept to a minimum.

This project will develop an innovative non-destructive technology to quantify the impact of the non-native parasite through measuring the acoustic resonance of the eel swim bladder. Knowing at what acoustic frequency the swim bladder resonates can help determine the size and wall thickness, two variables that help predict historic and current parasite load in individual fish. The project will deliver: 

1. Proof of concept: Design and construct a test chamber in which the acoustic resonance of live eel swim bladder is determined;  
2. Validation: Evaluate the effectiveness of the resonance tests to predict the dimension and physical properties of the swim bladder and parasite burden; 
3. Field testing and prototype development: Develop and test a prototype that can be used in the field.   

This project integrates the fields of acoustics, bioengineering, and conservation biology in a reciprocal way to deliver transformative applied technology. It can not be achieved through adopting a unidisciplinary approach. The International Council for the Exploration of the Sea states that eel recovery must be developed as a matter of urgency and this project will help in international goals to halt and reverse biodiversity loss. 

Subject Areas: acoustic resonance, swim bladder, anguilla, parasite, fish conservation, sound

Previous knowledge required:

• This highly interdisciplinary project would be suited to those from either a biology or engineering / physics based background who are adventurous enough to bridge the disciplinary divide to help protect nature.

• You should have or expect to be awarded a 2.1 (minimum) BSc or Master’s in Biology, Physics, Engineering or Acoustics.

• Project Supervisors: Dr David Angland  and Dr Chaitanya Paruchuri
• Industry Partner: Airbus SAS

Project Outline:
This project addresses broadband interaction noise, one of the key contributors to community noise from modern propeller aircraft. By developing accurate predictive tools, the project will help Airbus design quieter and more environmentally friendly aircraft. The outcomes will promote sustainability by contributing to quieter communities around airports.

Further project information:
This PhD project investigates the complex aerodynamic and acoustic interactions between a propeller and a downstream wing operating at high incidence angles. The research aims to deepen understanding of these interaction effects by addressing key questions on model accuracy, wake behaviour, and aerodynamic coupling.

The study will involve a combination of experimental and numerical methods. Wind tunnel experiments will be conducted to identify and characterize noise sources across a range of inflow conditions, using realistic high-lift wing geometries. A numerical framework will be developed to simulate propeller-wing interactions and validate with measured experimental data.

The final objective is to validate and refine existing analytical models to improve their predictive capabilities in complex, high-incidence scenarios. This research supports Airbus’ design and operational goals by enhancing the accuracy of noise prediction tools used in the development of the next-generation of aircraft.

Subject Areas: Aerospace Engineering, Fluid Mechanics, Computational Mathematics, Mathematical Modelling

Previous knowledge required:

• You should have or expect to be awarded 2.1 (minimum) BSc or Masters in Aerospace Engineering, Fluid Mechanics, Mathematics.
• Some prior experience of CFD and/or wind tunnel experience.  

• Project Supervisors: Dr Sergi Palleja Cabre  and Emeritus Professor Rod Self
• Industry Partner: Rolls-Royce

Project Outline:
This PhD project focuses on improving aircraft noise prediction for emerging technologies at early design stages. It involves developing whole-aircraft noise models, incorporating operational factors and fleet-level scenarios. The research will support Rolls-Royce’s noise prediction systems and inform certification standards and airport noise policies.

Further project information:
This PhD project focuses on advancing noise prediction and mitigation methods for emerging aircraft technologies, including next-generation gas turbine architectures and propeller-based propulsion systems. A key challenge is estimating noise emissions during the conceptual design phase, where trade-offs between noise and performance are often overlooked. The project will build on ongoing research at ISVR to adapt existing scaling laws for different noise sources, ensuring their relevance to novel aircraft configurations. It will also involve developing whole-aircraft noise models that incorporate operational characteristics such as take-off profiles, power requirements, and flight trajectories. These models will be extended to fleet-level scenarios, enabling assessment of noise impacts across various aircraft categories and compositions. The aviation industry stands at a pivotal moment, with disruptive concepts such as UltraFan and RISE redefining the future of flight. This PhD places you at the forefront of that transformation, shaping the next generation of sustainable propulsion. 

This PhD project will be hosted within the Rolls-Royce University Technology Centre (UTC) in Propulsion Systems Noise at the Institute of Sound and Vibration Research (ISVR), University of Southampton. The UTC is a world-leading hub for aeroacoustics research, offering close collaboration with the Rolls-Royce noise engineering team. This provides a unique opportunity to contribute to real-world challenges and make a tangible impact on the future of quieter, more sustainable aviation.

Note: This project is open to fully UK nationals and students from the EU and Horizon Europe-associated countries.

Subject Areas: Acoustics, Acoustics Engineering, Aerospace Engineering, Fluid Mechanics, Mechanical Engineering, Applied Mathematics, Mathematical Modelling

Previous knowledge required:

• A UK 2:1 honours degree or its international equivalent in a maths, physics or engineering subject. 
• It is advantageous to have some experience in aerospace engineering, particularly in flight mechanics,  aircraft operations or aeroacoustics.
• Coding experience in MATLAB or Python is also advantageous. 

• Project Supervisors: Dr Sifeng Bi and Dr Daniil Yurchenko

Project Outline:
Vibroacoustic modeling is crucial in engineering systems for accurately predicting, monitoring, and controlling noise and vibration, especially in aerospace and automotive applications. Virtual decoupling techniques have been successfully applied to isolate and analyze vibroacoustic systems in low-frequency domain. However, uncertainties often limit the model’s accuracy. Furthermore, vibroacoustic systems pose challenges due to the absence of a single analysis technique suitable for all frequency ranges. Different physical behaviors and uncertainty impacts in low-, mid-, and high-frequency domains necessitate distinct approaches. Stochastic model updating methods combined with uncertainty quantification provide a systematic framework to address these issues and enhance the reliability of vibroacoustic models. This project aims to integrate these methods, developing a comprehensive digital twin framework for the calibration, verification, and validation (V&V) of vibroacoustic models under uncertainty.

Further project information:
The objective is to develop a data-driven digital twin framework for vibroacoustic modeling across different frequency domains. This framework will integrate suitable combinations of techniques for low, mid, and high frequencies and incorporate dedicated model calibration and V&V approaches tailored to the specific features and uncertainty challenges of each frequency domain.

Proposed Work Packages:

WP1: Uncertainty Quantification (UQ) for Vibroacoustic Modelling
Tailor UQ techniques for each frequency domain: FEM and Modal Analysis for low frequencies, FEM-SEA and WFEM for mid frequencies, and SEA, Ray Tracing, and Geometrical Acoustics for high frequencies, addressing uncertainties specific to each domain.

WP2: Model Updating for Different Frequency Domains
Develop and apply model updating techniques for vibroacoustic systems across frequency domains, improving accuracy by incorporating experimental data and adjusting for uncertainties in each range.

WP3: Data-driven Approach for Efficiency and Applicability
Use data-driven techniques to enhance computational efficiency, reducing model complexity and making the framework more practical for real-world applications, especially in real-time settings.

WP4: Verification and Validation (V&V)
Implement a rigorous V&V process by comparing model predictions with experimental data, ensuring accuracy across all frequency domains, and addressing specific uncertainty challenges for each range.

Subject areas: Probabilistic theory, Finite element analysis, Machine learning

Previous knowledge required:
You are expected to have or obtain a 2:1 (minimum) BSc/BEng in Aerospace, Mechanical or Civil, Engineering 

• Project Supervisors: Dr Chaitanya Paruchuri, Dr Michal Kalkowski, and Dr Daniel Stabler

Project Outline:
Bees are essential pollinators supporting global food production and ecosystem health. While much is known about visual and chemical cues in bee behavior, the role of sound remains understudied. This project investigates how acoustic factors influence bee communication, foraging behavior, and pollination efficiency in natural and agricultural landscapes. The research will assess how emerging anthropogenic sounds, including those generated by drones and Urban Air Mobility (UAM) vehicles, may interfere with bee activity and pollination success. The outcomes will inform sustainable agricultural and urban planning strategies that protect pollinator health and maintain ecosystem resilience

Further project information:
This project addresses the core research question: How do anthropogenic sound sources, including drones and Urban Air Mobility (UAM) vehicles, affect bee behavior, communication, and pollination efficiency? Bees rely on wingbeat frequencies for a range of essential tasks, from intra-colony communication and thermoregulation to buzz pollination in crops such as tomatoes and blueberries. However, increasing noise pollution in the 50–2000 Hz range overlaps with bee acoustic signals, potentially disrupting these processes.

The project will use a combination of bioacoustic field recordings, soundscape mapping, and laboratory experiments in controlled acoustic environments (anechoic chambers) to investigate behavioral and physiological responses of bees to different sound profiles. High-speed video and acoustic sensors will be used to quantify changes in foraging behavior, flower visitation, and pollen release under varying sound conditions. Data will be analyzed using signal processing and behavioral modelling techniques to identify key acoustic parameters influencing pollination outcomes. The project’s outcomes will inform strategies for mitigating the impacts of noise on pollinators and support the design of quieter agricultural and aerial technologies.

Subject areas: Acoustics, biology, ecology, bioacoustics, environmental science, Signal processing

Previous knowledge required:

• A first-class or upper second-class degree (or equivalent) in Acoustics, biology, ecology, bioacoustics, environmental science, or a related discipline.

• Skills in data analysis (e.g. R, Python, or MATLAB) and/or signal processing would be advantageous.

• Project Supervisors: Dr Rafael Mestre,

Project Outline:
This PhD project aims to develop machine learning (ML) and signal processing techniques for analysing data from Distributed Acoustic Sensing (DAS) systems in urban environments. DAS technology, which uses fibre optic cables to detect acoustic signals over large distances and with extreme temporal, frequency and spatial sensitivity, offers real-time monitoring for applications such as traffic flow, pipeline integrity, and noise pollution detection. The primary challenge is efficiently processing the vast amount of data generated by DAS, especially in noisy and complex urban settings.

Further project information:

You will develop novel ML models combined with classical signal processing techniques to detect and classify key events such as pipeline leaks, traffic congestion, or sound pollution using this acoustic data at different frequencies. These models will be designed to handle noisy data and operate in real-time, making them suitable for urban monitoring systems. The specific application of DAS, whether focused on traffic, infrastructure, or environmental monitoring, will remain flexible and open for the student to shape based on their research interests and the needs of the project.

This project will be embedded in an ongoing grant that provides access to DAS data collected from urban environments in Southampton and London, allowing the student to test and refine the developed models in real-world scenarios. Additionally, you will have the opportunity to engage in interdisciplinary collaboration with researchers working in fields from the social sciences to the humanities. This will provide a broader perspective on how DAS technology can be applied across various sectors, enabling you to explore diverse applications and work in a multidisciplinary research environment.

Subject areas: Computer Science, Artificial Intelligence, Signal Processing

NOTE: This project is only available to UK Home fees applicants.

Previous knowledge required:

• A background in Computer Science or Artificial Intelligence, or some understanding and experience working with machine learning techniques.
• Experience with signal processing techniques is also encouraged.
• Experience or knowledge of distributing sensing is not a requirement, but would be valued positively.