Swansea University, Wales, United Kingdom invites online Application for number of Fully Funded PhD Positions at various Departments. We are providing a list of Fully Funded PhD Programs available at Swansea University, Wales, United Kingdom.
Eligible candidate may Apply as soon as possible.
(01) PhD Positions – Fully Funded
PhD position summary/title: Applied Mathematics: Fully Funded PhD Studentship in Agent-Based Infection Modelling for HIV Treatment Optimisation (RS911)
HIV remains one of the most persistent and challenging infections to cure due to its ability to lie dormant in reservoirs within the body: T-cells that are infected but no actively replicating virus. This allows the virus to evade standard treatment approaches. Despite years of antiretroviral therapy (ART), complete eradication of the virus is hindered by the presence of these reservoirs, which require continuous lifetime treatment. Recent efforts to cure HIV infection have focused on developing latency reversing agents as a first step to eradicate the latent reservoir and animal models are being studied to understand these dynamics.
This project proposes a novel pipeline of ideas to generate tools and techniques to simulate HIV infection dynamics using a multiscale agent-based modelling technique (cells, viruses, drugs, antibodies, human lymph system, seconds, days, years).
This project is in collaboration with GSK and we will work closely with experts from GSK.
The Departments of Mathematics is part of the Computational Foundry, a world-class centre for computational research, part-funded by the European Regional Development Fund through Welsh Government. The brand-new building of the Computational Foundry provides an ideal environment for doing a PhD.
Deadline :5 January 2026
(02) PhD Positions- Fully Funded
PhD position summary/title: Psychology: Fully Funded PhD Studentship in Resolving the Bilingualism Language Change Paradox (RS919)
With Wales being a bilingual society, it is common for many people to use both Welsh and English when interacting with other people. When a bilingual is communicating with another person who speaks the same languages (e.g., Welsh and English), bilinguals switch languages interchangeably, often midsentence. The fact that bilinguals voluntarily switch when communicating appears at odds with much laboratory based research suggesting that switching language comes at a cognitive cost. Which begs the question: why do bilinguals choose to keep switching language types in conversation, despite apparent information processing inefficiencies? A phenomena we have dubbed the bilingual communication “paradox”. It is clear that understanding how and why people voluntarily switch languages remains a key issue in studying the bilingual experience – and evidence suggests such switching behaviour can be influenced by many different factors. This can include cues present in the context (e.g., visual information associated with one of the languages, de Bruin & Martin, 2022; Vaughan-Evans, 2023) as well as the language behaviour of the conversation partner (Kootstra et al., 2020). Currently, such emerging evidence has not been studied in detail with Welsh-English bilinguals, despite Welsh speakers being an ideal population for investigation. It is our view that Wales and Welsh speakers constitute an untapped ‘living laboratory’ of bilingualism research, and we are keen to demonstrate this untapped potential to the wider research world relevant to this topic.
Deadline : 12 January 2026
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(03) PhD Positions – Fully Funded
PhD position summary/title: Welsh, Sociolinguistics: Fully Funded PhD in Understanding and Supporting Adult Motivation to Learn Welsh (RS917)
Although large numbers of adults attend Welsh for Adults classes nationally, statistics from 2019–2024 show that only around 26% progress beyond Entry level (CEFR A1; National Centre for Learning Welsh, 2025). Increasing these progression rates is a key aim of the Welsh Government’s strategy to reach one million speakers by 2050 (Welsh Government, 2017; 2025). Achieving this requires a deeper understanding of learners’ changing motivations, and ensuring that Welsh tutors draw on current evidence to sustain learner engagement over time.
Motivation is a major factor in second language acquisition (Yousefi & Mahmoodi, 2022). The L2 Motivational Self System (L2MSS; Dörnyei, 2005) is widely used to explain variation in learners’ motivation. However, questions have been raised about its suitability for languages other than English and for minority-language contexts (Dörnyei & Al-Hoorie, 2017; Zhu, 2019). There is very limited research on the motivations of adult Welsh learners, and no studies have yet applied the L2MSS to Welsh. This project will address this gap by developing a systematic understanding of adult Welsh learners’ motivations and evaluating the suitability of contemporary motivational theories for a minority indigenous language context.
Deadline : 12 January 2026
(04) PhD Positions – Fully Funded
PhD position summary/title: Civil Engineering: Fully Funded PhD studentship in AI-Assisted Mesh Generation and Adaptation for Industrial Simulation (RS928)
In many engineering simulations, the accuracy and efficiency of the solution depend critically on how the mesh is distributed relative to the underlying physics. Features such as boundary layers, shocks, vortices, thermal gradients and structural stresses often occur in regions that are closely linked to the geometry of the problem. In current industrial workflows, these phenomena are commonly captured either by globally over refining the mesh, which is computationally expensive and environmentally inefficient, or by running multiple successive simulations to iteratively adjust the mesh. Both approaches raise computational cost, energy consumption and turnaround time, placing increasing pressure on sustainability targets. Understanding how geometric changes influence the flow, thermal or structural response remains a major challenge, and traditional mesh spacing strategies struggle to capture the complex, nonlinear ways that geometry shapes multiphysics behaviour, leading to either unnecessary refinement or a loss of fidelity in critical regions.
Machine learning provides a promising route to capture these relationships more systematically by identifying how local geometric features determine the resolution required for reliable prediction. A central goal of this project is to learn how solution fields and mesh resolution requirements vary with geometric change, enabling sensitivity informed meshes that adapt to both physical behaviour and geometric context. The project will develop a machine learning framework that learns the link between geometric variation, coupled physical responses, solution sensitivity and mesh spacing requirements. The resulting tools will support automated mesh generation and adaptation, reduce manual tuning and improve the reliability of simulations involving geometry driven behaviour across multiple physical models.
As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the opportunity to explore realistic engineering configurations and gain expertise in areas that are rapidly growing yet still rare across the simulation community, including AI-assisted mesh generation and adaptation for industrial simulation. The skills developed through this work will align strongly with careers in scientific computing, engineering simulation and applied machine learning for design and analysis.
Deadline : 2 February 2026
(05) PhD Positions – Fully Funded
PhD position summary/title: Civil Engineering: Fully Funded PhD studentship in High-Order Mesh Generation for Complex Industrial Geometries with Multiscale Features (RS927)
High-order solvers offer clear accuracy advantages, yet their effectiveness is fundamentally limited by the availability of suitable high-order meshes for complex industrial geometries. Current workflows rely heavily on geometric de-featuring, an expert-driven, manual, and time-consuming process used to simplify CAD models so that meshing tools can cope with small-scale features such as fillets and manufacturing details. This de-featuring is not only expensive but also problem-dependent, as the relevance of a geometric detail varies with the physics being simulated. Concepts such as virtual topology have emerged to make meshing more flexible by allowing elements to span across multiple CAD faces without explicitly modifying the geometry. However, these ideas have not yet been developed in high-order settings, where curved elements, geometric fidelity, and element quality requirements make the problem considerably harder.
The aim of this project is to develop new methodologies and software tools that enable high-order meshes to be generated automatically for large, intricate industrial geometries without requiring extensive de-featuring. The key idea is to allow high-order elements to traverse multiple CAD surfaces, extending the virtual topology concept to curved, high-order discretisation, while preserving accuracy, mesh validity, and robustness. This will involve advances in CAD–CAE interfacing, curved element optimisation, multiscale geometric feature detection, and error-controlled surface approximation. The resulting technology will be tested on real industrial geometries provided by partners in aerospace and energy such as Airbus and UKAES, where current workflows still depend on substantial manual model clean-up. By removing the reliance on de-featuring and enabling reliable high-order meshing of complex shapes, the project will substantially shorten preparation times and accelerate the adoption of high-order methods in industrial design and analysis.
As the PhD researcher on this project, you will develop the numerical, geometric and algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically focused research group that maintains regular interaction with major industrial partners, giving you direct exposure to the challenges companies face when preparing large CAD models for high fidelity simulation. This project will allow you to work with demanding real-world geometries and to build specialist expertise in high order mesh generation, geometric modelling and CAD to CAE integration. These capabilities are of growing importance to industry yet remain available to only a small number of researchers, providing you with a rare and highly valued skill set that aligns strongly with careers in scientific computing and engineering simulation.
Deadline : 2 February 2026
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(06) PhD Positions- Fully Funded
PhD position summary/title: Civil Engineering: Fully Funded PhD studentship in Hybrid High-Fidelity CFD for Industry: Next-Generation Methods for Accurate and Efficient Aerodynamic Simulat (RS926)
High-order discontinuous Galerkin methods are gaining traction in industrial aerodynamic simulation workflows, including those at Airbus and Dassault Aviation, because they offer significantly improved accuracy for unsteady flow problems. Their principal strength is the lower numerical dispersion and dissipation they introduce compared with the low-order finite volume and finite element schemes that currently dominate industrial solvers. In practical terms, this means that vortices, waves, and other flow disturbances can travel long distances without losing their shape or energy artificially, which is essential for high-fidelity transient simulations. However, despite these advantages, the widespread industrial use of high-order methods remains limited, largely because their integration into established development pipelines requires changes in meshing, solver technology, and verification practices.
The project is designed to reduce this barrier by creating a practical approach that blends existing industrial strengths in low-order mesh generation with the benefits of high-order accuracy. While low-order meshes are straightforward to produce and well supported by mature tools and workflows, generating high-order curved meshes of arbitrary polynomial order is considerably more complex, especially around intricate aerodynamic surfaces. Instead of replacing current industrial meshing practices, the project proposes a hybrid strategy: low-order schemes will continue to be used in the near-field region around aerodynamic obstacles, where mesh generation is well understood and geometrically demanding, while high-order methods will be applied in the far-field, where the flow is smoother and mesh curvature requirements are less stringent. By combining the robustness of low-order meshes with the superior accuracy of high-order techniques, the project will enable meshes originally created for steady-state simulations to be repurposed effectively in transient scenarios, thereby increasing efficiency and accelerating industrial adoption of high-order CFD technologies.
As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical for aerodynamic simulation. You will work within a technically focused research group that maintains active engagement with industrial teams exploring next generation CFD technologies, giving you direct insight into real development workflows and emerging technologies in the engineering sector. Through this project you will gain specialist expertise in high order flow simulation, hybrid numerical strategies and large-scale transient solvers, areas that are becoming increasingly important yet remain uncommon across the wider simulation community. You will have the opportunity to evaluate ideas on complex, realistic configurations and develop a skill set that is highly valued in both academic research and advanced engineering computation.
Deadline : 2 February 2026
(07) PhD Positions – Fully Funded
PhD position summary/title: Computer Science: Fully Funded PhD studentship in Zero-knowledge Succinct Non-interactive Argument of Knowledge for Public Good (RS915)
Recently, many councils in Wales started using single transferable vote (STV) method for counting ballots. While counting plaintext ballots using STV method is straightforward, but a rank-based ballot may leak the voter’s preferences if published publicly in plaintext, as the ranking order can reveal detailed information about the voter’s identity. Therefore, encryption is necessary to hide the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballots using the STV method. Our first point of investigation will be zero-knowledge succint non-interactive argument of knowledge– ZkSNARK. Subsequently, we will formalise the front-end (R1CS) and back-end (Groth16) of ZkSNARK in the Coq theorem prover and use this formalisation to encode our STV algorithm on encrypted ballots. This approach aims to ensure both the correctness and privacy of the tallying process, paving the way for verifiable and secure election systems that are resistant to coercion.
Deadline : 2 February 2026
(08) PhD Positions – Fully Funded
PhD position summary/title: Data Driven Microgrid Control : Fully Funded PhD Scholarship in Data-driven Microgrid Control (RS908)
To combat climate change and achieve the UK’s target of Net Zero, it is expected that the integration of renewable energy sources (RESs) at the distribution/consumption level will keep increasing. The volatile and intermittent nature of RESs causes significant difficulties for the network operator to balance generation with demand and maintain power quality, which makes the network prone to instability and blackouts. In addition to their volatile nature, RESs cannot provide the ancillary services (such as voltage and frequency control) that conventional synchronous generators naturally deliver, exacerbating the situation as the penetration of RES increases, especially at the distribution level.
In this context, microgrids (MGs) refer to clusters of consumers, prosumers (consumers + producers), energy storage systems (ESSs), and electric vehicles (EVs) that collectively form a local energy community (EC). ECs are supposed to facilitate direct peer-to-peer (P2P) energy trading mechanisms to optimize objectives such as reduced bills, reduced emissions, or minimization of the exchanged energy with the grid. Such ECs can also potentially provide ancillary services to the grid, such as power balancing, peak shaving/shifting, voltage and frequency support, and virtual inertial response.
Due to the volatile and intermittent nature of RESs, in this project, machine learning (ML) methods are used to accurately forecast local generation and demand. To do so, historic local data (e.g., the active buildings in Swansea University) and Met Office data will be used to train and validate the proposed ML model. These forecasted data will then be used to propose and optimize an energy management strategy for an EC comprising a number of prosumers, consumers, ESSs, and EVs. Different vehicle-to-home and vehicle-to-community energy trading strategies will also be proposed and investigated to achieve optimized P2P trading within the EC.
Deadline : 2 February 2026
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(09) PhD Positions – Fully Funded
PhD position summary/title: Experimental Physics: Fully funded, 3.5-year PhD Studentship in Antihydrogen Physics with the ALPHA Experiment at CERN (RS922)
This project aims to use laser-cooled beryllium ions to improve antihydrogen experiments in the ALPHA apparatus at CERN. The ALPHA collaboration is world-leading in studies of antihydrogen as a probe of matter and antimatter symmetry. Antihydrogen is made by merging cold antiprotons and positrons in a Penning-Malmberg trap. The antihydrogen atoms are trapped in a magnetic minimum trap where they can be exposed to both microwave and laser fields to probe their energy structure, or to carefully tailored magnetic fields to reveal their behaviour due to gravity. Laser-cooled beryllium ions trapped in the ALPHA apparatus have become crucial to antihydrogen physics. The ions are used to sympathetically cool positrons for antihydrogen production and thereby make colder antihydrogen, thus increasing the trapping rate and improving the statistics and capabilities of the experiment. We can also use cold beryllium ions as an absolute in-situ AC and DC magnetometer to increase our knowledge of the magnetic trapping fields and to probe the strength of the microwave fields used for inducing hyperfine transitions. The successful candidate will focus on one or more of the above aspects and take them to their full potential. This studentship is based at the ALPHA experiment at CERN in Geneva, Switzerland.
Deadline : 2 February 2026
(10) PhD Positions – Fully Funded
PhD position summary/title: Experimental Physics: Fully funded, 3.5-year PhD Studentship in Antihydrogen Physics with the ALPHA Experiment at CERN (RS923)
This project aims to improve the precision of antihydrogen spectroscopy in the ALPHA apparatus at CERN. The ALPHA collaboration is world-leading in studies of antihydrogen as a probe of matter and antimatter symmetry. Antihydrogen is made by merging cold antiprotons and positrons in a Penning-Malmberg trap. The antihydrogen atoms are trapped in a magnetic minimum trap where they can be exposed to both microwave and laser fields to probe their energy structure, or to carefully tailored magnetic fields to reveal their behaviour due to gravity. Aided by laser-cooled beryllium ions and laser cooling of the formed antihydrogen, the process now produces large ultracold samples of antihydrogen, which allows determining the energy spectrum of antihydrogen with improved sensitivity. Comparing the result with ordinary hydrogen constitutes a stringent test of fundamental symmetry. Most notably, the 1S-2S energy interval has been shown to agree with hydrogen to a few parts in a trillion, with the uncertainty dominated by antihydrogen. Supported by state-of-the-art frequency metrology, the collaboration now aims to improve the uncertainty in antihydrogen to reproduce or surpass the result in hydrogen to achieve the most precise test of fundamental symmetry to date. The excited state spectrum can also be probed, granting access to precision measurements of fundamental properties of the nucleus such as the antiproton charge radius. The successful candidate will focus on one or more of the above aspects and take them to their full potential. This studentship is based at the ALPHA experiment at CERN in Geneva, Switzerland.
Deadline : 2 February 2026
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(11) PhD Positions – Fully Funded
PhD position summary/title: Mechanics, Structural Vibration: Fully Funded PhD Studentship in Digital approaches to nonlinear structural dynamic modelling (RS909)
Identifying and validating models for complex structures featuring nonlinearity remains a cutting-edge challenge in structural dynamics, with applications spanning civil structures, microelectronics, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust, interpretable models from experimental and operational data.
The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that improves the performance of ROMs, making them more applicable to real-time structural health monitoring, vibration analysis, and control design.
Deadline : 2 February 2026
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(12) PhD Positions – Fully Funded
PhD position summary/title: Sport and Exercise Sciences: Fully Funded PhD studentship in Sports Science, Performance Science/Skill Acquisition/Biomechanics & Technology (RS914)
This studentship aims to develop valid and high-fidelity immersive simulations to support the transfer of performance to competitive sporting environments. Immersive simulation is widely used in healthcare teaching and training, and in 2023 Swansea University opened a new Simulation and Immersive Learning Centre (SUSIM) for this purpose. The Department of Sport and Exercise Sciences have been collaborating with the SUSIM team to explore the potential for applications of immersive wall technology to support skill acquisition, training and testing in performance sport. This proposed collaborative PhD between Swansea University and the University of Canberra aims to complement and enhance an existing project at Swansea University that has scoped similar applications across other domains and is exploring the potential for this technology to elicit realistic emotional responses in simulated sporting environments. The proposed PhD will explore new potential sporting applications, develop new simulations, and evaluate the validity (particularly face and construct) and fidelity of these simulations in order to provide an evidence base to support their implementation into high performance sport.
The PhD is jointly funded by Swansea University and the University of Canberra. Both universities have committed to match funding the project, and there will be supervisory input from both Universities from the outset. The successful candidate will initially enrol on a Swansea University PhD, and when a formal agreement between the two Universities is finalised, the successful candidate will have the opportunity to transfer enrolment to the PhD programme agreed by both Universities. The studentship will be hosted at Swansea University given the location of the SUSIM Centre. Whilst time at the University of Canberra is not an expectation of this studentship, the student will have the opportunity to spend some time at the University of Canberra if they wish and it is valuable for the project and/or their development, but there is currently no additional funding (to that listed below) allocated to support this travel.
Deadline : 2 February 2026
(13) PhD Positions – Fully Funded
PhD position summary/title: Materials Engineering: Fully Funded PhD Studentship in Advancing Sustainable Solar Solutions to Transform Global Energy Access (RS934)
Working as part of Swansea University’s UNESCO Chair in Sustainable Energy Technologies and the REACH-PSM project (Resilient Renewable Energy Access Through Community-Driven Holistic Development in Perovskite Solar Module Manufacturing), this PhD offers an exciting opportunity to contribute to the development of innovative, sustainable solar energy solutions.
The UNESCO Chair addresses the urgent need for equitable and sustainable energy access, fostering global partnerships to develop renewable energy technologies that align with the United Nations’ Sustainable Development Goals. The REACH-PSM project complements this mission by focusing on community-driven approaches to perovskite solar module (PSM) manufacturing, ensuring energy independence and resilience in underserved regions, particularly across the continent of Africa. Together, these initiatives aim to revolutionise renewable energy access by integrating cutting-edge technology with social and environmental sustainability.
Deadline : 2 February 2026
About Swansea University, Wales, United Kingdom –Official Website
Swansea University (Welsh: Prifysgol Abertawe) is a public research university located in Swansea, Wales, United Kingdom. It was chartered as University College of Swansea in 1920, as the fourth college of the University of Wales. In 1996, it changed its name to the University of Wales Swansea following structural changes within the University of Wales. The title of Swansea University was formally adopted on 1 September 2007 when the University of Wales became a non-membership confederal institution and the former members became universities in their own right.
Swansea University has three faculties across its two campuses which are located on the coastline of Swansea Bay. The Singleton Park Campus is set in the grounds of Singleton Park to the west of Swansea city centre. The £450 million Bay Campus, which opened in September 2015, is located next to Jersey Marine Beach to the east of Swansea in the Neath Port Talbot area. The annual income of the institution for 2021–22 was £369.9 million of which £69.2 million was from research grants and contracts, with an expenditure of £446.3 million.
It is the third largest university in Wales in terms of number of students. It offers about 450 undergraduate courses, 280 postgraduate taught and 150 postgraduate research courses to 20,375 undergraduate and postgraduate students.
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