University of Liverpool, England invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at University of Liverpool, England.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Using mixed methods to understand how the food environment can be changed to improve diet and health
The PhD will use a combination of methods and generate evidence which will inform local and national public health policy in the UK. The PhD will have two main parts. First, the successful applicant will contribute to a new trial testing whether it is feasible for small out of home food businesses to adopt menu calorie labelling and what public health benefit this could have. This will involve learning about mixed methods approaches to research and then applying those skills to work with participants and food outlets in the local community. Second, the PhD will investigate how food advertising shapes eating behaviour, and how this might change if advertising is restricted. There will be the opportunity to contribute to a randomised controlled trial (RCT) investigating the impact of brand only food advertising and conduct experimental studies examining the psychological processes through which advertising may influence behaviour.
The PhD student will be encouraged to develop their own ideas and research studies alongside supportive academic supervisors. The position would be ideal for a psychology graduate with an interest in research that has potential to improve health and bring about real-world change. Relevant research experience is desirable. The supervisory team have an excellent record of supervising PhD students and helping early career researchers transition into a long-term research career.
Deadline : 1 March 2026
(02) PhD Degree – Fully Funded
PhD position summary/title: Defining and measuring success in responses to missing children. Developing a multidimensional outcomes framework.
Children under 18 account for approximately 63% of the 330,000+ missing person reports made to UK police annually, with up to 75% involving repeat episodes. Similar trends are evident internationally, highlighting the systemic and persistent nature of missing children as a safeguarding issue. Children who go missing face heightened risk of harm, including criminal and sexual exploitation. Going missing also contributes to a range of long-term adverse outcomes, including increased vulnerability to victimisation, poor mental health, and disruption to education and employment trajectories. Children in local authority care are disproportionately affected, being three times more likely to go missing and to do so repeatedly compared to peers.
Although responsibility for responding to missing children is shared across multiple agencies, the police continue to bear a disproportionate burden. Responses are estimated to cost more than £394–£509 million annually. This reactive, policing-led model has been criticised for inefficiency, criminalisation of vulnerable children, and growing unsustainability as cases continue to rise. There is growing recognition of the need for a more preventative, multi-agency response, yet approaches vary widely across regions.
Research has begun to examine these variations to identify “what works” in preventing and responding to missing children. However, success is often measured narrowly using police metrics, such as whether a child is found, how quickly, and whether repeat episodes occur. These measures rarely capture the significance or broader impact of outcomes. For example, some evaluations of dedicated policing teams show that children were found faster but went missing more often. Those labelled “no apparent risk” remained missing longer, but the impact of prolonged absence is unclear. Such metrics overlook the complexity of outcomes, including children’s physical and psychological wellbeing, long-term safeguarding, family support, and systemic change. They also fail to account for context.
Deadline : 20 March 2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: Optimizing Low-Dose Mobile 3D X-ray Imaging for Clinical Applications
Digital tomosynthesis (DT) offers rapid, low-dose 3D imaging using compact and mobile X-ray systems, filling the performance gap between planar radiography and full CT. The QUASAR Group has collaborated with Adaptix Ltd for more than a decade to develop novel 3D imaging technologies, including the recently FDA-cleared Adaptix Ortho350 extremity imaging system. Related systems have already been commercialised in veterinary and industrial imaging.
This PhD project will build on the new SCIMITAR framework (Hill et al., Biomed. Phys. Eng. Express, 2025), which integrates geometric simulation with genetic-algorithm optimisation to design and evaluate next-generation chest DT devices. You will work within a multidisciplinary team with expertise in simulation, medical physics, imaging hardware, and AI-based reconstruction. The precise research direction will be defined collaboratively, but potential areas include:
- Simulation and digital twinning: extending SCIMITAR for full 3D optimisation, dose estimation, and patient-specific adaptation.
- Radiation transport modelling: using Monte Carlo and physics-based digital twins to evaluate imaging geometries, collimation strategies, and safety trade-offs.
- Novel source and detector technologies: investigating dual-energy approaches, alternative detector architectures, and cold-cathode (CNT) X-ray emitters in partnership with Adaptix Labs.
- AI-driven analysis: developing machine-learning algorithms for image reconstruction, artefact reduction, and automated feature detection from DT datasets.
- Synthetic patient populations: simulating diverse anatomies and imaging workflows to assess diagnostic accuracy and robustness.
- Experimental validation: acquiring data using phantoms and prototype Adaptix chest imaging systems, and exploring system miniaturisation, source motion strategies, and adaptive cone-angle designs.
Deadline : Open until filled
(04) PhD Degree – Fully Funded
PhD position summary/title: New Magnetic Particle Imaging (MPI) methods based on complex nanocomposite particle dynamics
Magnetic nanoparticles underpin a rapidly growing class of biomedical technologies, from non-invasive medical imaging to advanced biomaterials for tissue engineering. This PhD project will develop next-generation magnetic nanoparticle systems whose structure and dynamics change in response to their biological environment, enabling new ways to image and measure functional properties inside complex, opaque materials.
The project sits at the interface of chemistry, physics, and biomedical engineering, combining nanoparticle and hydrogel design with advanced magnetic imaging and sensing. It will exploit the University of Liverpool’s globally unique Magnetic Particle Imaging (MPI) infrastructure, including the only MPI scanner in the UK, alongside newly developed magnetic particle rheology and detection technologies.
The central scientific idea is that the magnetic response of nanoparticles depends sensitively on their rotation, aggregation state, and local mechanical environment. By designing nanoparticles and nanocomposites that undergo controlled structural changes, such as clustering, polymer rearrangement, or matrix-driven restriction of motion, you will create systems whose magnetic signals directly report on biological triggers (e.g. pH, enzymes, binding events) or local material rheology.
Deadline : 28 February 2026
(05) PhD Degree – Fully Funded
PhD position summary/title: Magnetic nanoparticle detection methods for imaging and diagnostics
Magnetic particle imaging (MPI) is an emerging imaging modality, detecting nanomolar concentrations of magnetic nanoparticle tracers for medical imaging and diagnostics. MPI is particularly appealing as it can provide highly sensitive, quantitative imaging in areas where MRI struggles. The methodology of MPI is to detect the non-linear response, typically of Superparamagnetic Iron Oxide Nanoparticles (SPIONs) tracers, under application of a sinusoidal drive magnetic field. More broadly, such magnetic nanoparticles underpin a rapidly growing class of biomedical technologies, from non-invasive medical imaging to advanced biomaterials for tissue engineering. A key opportunity for MPI and wider applications is the co-development of new high-throughput magnetic nanoparticle detection and characterisation methods that are optimised to respond to highly promising new multilayered magnetic materials.
This PhD project will work within a highly interdisciplinary team to develop next-generation magnetic nanoparticle detection methods in tandem with new magnetic nanoparticles that are functionally responsive to biological environment, enabling new ways to image and measure properties inside complex biomaterials.
The project sits at the interface of physics, chemistry, and biomedical engineering, combining nanoparticle design with advanced magnetic imaging and sensing. It will exploit the University of Liverpool’s globally unique Magnetic Particle Imaging (MPI) infrastructure, including the only MPI scanner in the UK, alongside newly developed magnetic particle rheology and detection technologies.
Deadline : 28 February 2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: Low-temperature reaction studies of ions and radicals
Gas-phase radicals (atoms or molecules with an unpaired electron) and ions (atoms or molecules with a net charge) are hugely influential in numerous areas of research. They are responsible for much of the chemistry occurring in the atmosphere, the interstellar medium, in plasmas, and in combustion processes. However, very few existing experimental methods can measure gas-phase radical or ion processes under cold and controlled conditions, resulting in significant unanswered questions across multiple fields. In the absence of experimental measurements, untested assumptions are included in databases and models, hindering the accuracy of their predictions. In this project, you will work on addressing this long-standing issue.
We use cold conditions and external fields to explore how reactive collisions occur. Cold environments – typically temperatures less than 1 Kelvin – allow us to control the properties of reactants. By manipulating the reaction conditions, we can unravel the role different parameters play in determining the outcome of a reactive collision. We use a number of techniques – including laser cooling, ion trapping and the application of external fields – to investigate reactions between ions and neutral species. We sensitively probe the reaction products using imaging and time-of-flight mass spectrometry detection methods.
Using these experimental approaches for the study of gas-phase radicals and ions, you will study reactions in systems relevant to astrochemistry, atmospheric chemistry, and surface science. Measurements will be performed using a unique magnetic guide (developed to examine the reactions of neutral radicals) and a novel low-temperature ion trap (to study processes involving molecular ions).
Deadline : 26 February 2026
(07) PhD Degree – Fully Funded
PhD position summary/title: Instrumentation studies for AWAKE Run 2c
The current AWAKE scientific program, called Run 2b, has produced several high impact results in recent years, including successful acceleration of the electron beam up to 2 GeV in a single stage of acceleration, demonstrated stable and reproducible seeded self-modulation (SSM) for a long proton bunch, and demonstrated the application of scalable plasma sources.
Run 2c has now been confirmed, which will use a second plasma channel and electron source to reach higher energies whilst maintaining and monitoring the beam quality for applications. This work will pave the way for this novel acceleration technology to drive compact machines towards the energy frontier.
Deadline : 19 December 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: Kantian Justice: A Desert-sensitive Responsibility-enhancing Theory [KantianDESERT]: PhD Studentship (ERC/UKRI)
KantianDESERT is designed to formulate a new model of distributive justice in response to growing global economic disparities, by offering a distinctive position within dominant egalitarianisms in current political theory/philosophy. First, despite calls in political theory/philosophy to abandon ‘desert’ (Barry 1965: 78; Kleinig 1971: 71; Rawls 1971/rev. ed. 1999), the project, in its first part, retrieves a strong critico-contestatory notion, which continues to guide us in our everyday distributive practices and denunciation of injustices. Secondly, against the background of a naturalist direction in academic disciplines (Scheffler 2001: 20-1) and in the context of a call for theories of justice to be ‘political, not metaphysical’ (Rawls 1985), the second sub-project of KantianDESERT answers important metaphysical objections from moral responsibility scepticism, by drawing on a new reconstruction of Kant’s account of freedom and moral agency. Thirdly, in the context of a recent revival of interest in desertism (Moriarty 2018; Brouwer and Mulligan 2019) and inspired by the second sub-project’s novel reconstruction of Kant’s theory of justice, the third sub-project argues for an innovative desert-sensitive theory of distribution, which takes into consideration other important standards of justice, such as equality, efficiency or need.
Deadline : 28 February 2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: New Tesla-valve-inspired heat exchanger based passive safety system for nuclear reactor
Nuclear energy is recognised as a green and environmentally sustainable source of power, where heat generated during the nuclear reaction is harnessed to drive the turbine and produce electricity. However, during emergency shutdowns or in the event of accidents, it is become critically important to safely remove decay heat from the reactor core to prevent reactor from overheating and ensure the sustainable and resilient operation of next-generation compact reactors.
Among various cooling strategies, the natural circulation loop (NCL) offers a promising passive approach for heat removal, as it functions without external power or mechanical components, enhancing reliability and safety. However, natural circulation is often difficult to control due to flow instabilities and its performance is strongly depend on efficiency of the heat exchanger. Therefore, enhancing flow controllability and heat transfer efficiency remains a major research challenge.
This project proposes the development and evaluation of a Tesla-valve-inspired heat exchanger, coupled with two-phase flow, to enhance the performance of natural circulation loop for safety system of nuclear reactor. The Tesla valve—a passive check valve without moving parts—suppresses reverse flow and improves flow directionality, while vapor bubble dynamics can further accelerate circulation. By integrating these concepts, the project aims to advance passive safety technologies for nuclear reactors, supporting SDG 9 by fostering innovation, building resilient infrastructure, and promoting sustainable industrial development in the clean energy sector.
Deadline : 15 March 2026
(10) PhD Degree – Fully Funded
PhD position summary/title: Met2Adapt: Modelling of the dynamic response for offshore wind farms
Funded by the Marie Skłodowska-Curie Actions (Horizon Europe, Grant Agreement No. 101227175), Met2Adapt aims to recruit 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.
Key to our training methodology is our firm commitment on establishing an active and student-centred ‘training by research’ environment, which will put the Doctoral Candidates in charge of their training process. On top of the supervision arrangements provided by the Met2Adapt academic beneficiaries, the DCs will join a thriving training hub comprising formal courses, dedicated training weeks, academic and industrial secondments, and a dedicated industrial mentorship program. The candidates will work together within the Met2Adapt network and engage in multi-disciplinary training-by-research to develop technology in close collaboration with end-users around the world.
The combination of a long coastline, shallow water, and offshore wind makes the UK one of the best locations in the world for wind power. As of 2023, the UK has over 11,000 wind turbines generating 30 GW of power, which accounts for almost 30% of the UK electricity supply. The UK Government has committed to increase the offshore capacity to 50 GW by 2030. Despite these significant benefits and enthusiasm for wind power, there are some serious challenges, which have become apparently in the last two decades 1) Clusters of turbines can produce powerful standing waves, in both air and seawater, severely affecting wildlife and radar and sonar operations. 2) Wind turbines are gyroscopic systems that possess highly complex chiral vibrational modes. DC9 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.
Deadline : 2 March 2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: Modelling the impacts of changing the food environment
What are the impacts of public health policy interventions aimed at changing food environments?
This fully funded PhD position will examine the impacts of public health policy interventions targeting the food environment in addressing obesity and diet-related illnesses. This PhD student will join the internationally recognised NCD Prevention and Food Policy Modelling Group and Theme at the Department of Public Health, Policy & Systems, whose work has shaped national and global prevention strategies through advanced population simulation modelling.
The PhD will develop their knowledge and research skills on public health nutrition, health policy, epidemiology, and simulation modelling to assess the population-level impacts of food-environment policy interventions, as well as the extent to which these policies may contribute to reducing health inequalities in the UK. The PhD will contribute to two main activities. First, the successful applicant will contribute to a local food intake survey to examine daily nutrient intake and understand the contributions of the out-of-home (OOH) and non-OOH sectors, as well as exposures to the obesogenic food environment. Second, the PhD students will use simulation models to quantify the effects of policy interventions aimed at improving the food environment. The PhD also offers substantial flexibility in choosing public health policy options (e.g., fiscal policy, food labelling, reformulation, policies affecting food availability and affordability) to be modelled. We encourage applicants to suggest policy interventions they are particularly interested in exploring during this PhD, as well as to check our previous studies to understand the type of policies that can be modelled.
Building on established IMPACT modelling approaches, the student will also analyse linked longitudinal data, nutritional survey data, risk factor trends, and disease trajectories to simulate the impacts of policy interventions on reducing disease burden and the associated future economic benefits. These models have previously informed WHO global sodium benchmarks, the redesign of the NHS Health Check, CMO reports, OECD analyses, and major national policy decisions. The PhD student will also be offered the opportunity to use other modelling approaches from our own group and collaborating institutes.
This funded PhD studentship is open to applicants with a strong quantitative background in a range of disciplines, including, but not limited to, public health, epidemiology, health psychology, data science, and demography. Relevant research experience and experience working with R software are desirable. Ideal candidates will also have a strong commitment to policy-relevant research and enthusiasm for tackling health inequalities through rigorous analytical and simulation modelling. The successful candidate will join a vibrant team with an excellent record of supervising PhD students, publishing in high-impact journals, and securing major funding (ERC, ESRC, NIHR, European Commission, NIH, Health Foundation).
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(12) PhD Degree – Fully Funded
PhD position summary/title: Visualizing Place: Using Multimodal AI to Describe and Represent Geodemographic Classifications
This PhD will investigate how large language models and multimodal AI can enhance the interpretation and communication of geodemographic classifications. Central research questions address how LLMs can generate accurate, contextually appropriate narrative descriptions of geodemographic clusters; how combining LLMs with vision language models can improve the interpretability of complex demographic and geographic data; what limitations, biases, and ethical considerations arise when deploying generative AI for geodemographic characterisation; and how these approaches can be operationalised for policy and planning applications. Key research avenues include fine-tuning or prompt-engineering LLMs to produce coherent descriptions of geodemographic archetypes, evaluating output quality, accuracy, and reproducibility across different model architectures, and using VLMs to analyse satellite imagery, aerial photographs, and street-level data to extract semantic features such as urban density, vegetation, and built environment characteristics that can be linked to geodemographic profiles. A further strand explores the use of text-to-image models to generate representative visual archetypes for each classification, developing prompt strategies that accurately reflect cluster characteristics, evaluating stakeholder responses to AI-generated imagery compared with traditional statistical visualisations, and critically examining how such imagery might reinforce or challenge stereotypes. Methodological considerations span data integration combining traditional geodemographic variables with AI-derived interpretations from unstructured sources, bias assessment and mitigation across representational, algorithmic, and output dimensions, and ensuring explainability, transparency, and reproducibility in all outputs. Finally, the research envisages collaboration with policymakers and agencies in urban planning, public health, and transport to prototype communication tools and develop best-practice guidelines for the responsible use of generative AI in geodemographic research.
Deadline : 27 February 2026
(13) PhD Degree – Fully Funded
PhD position summary/title: Strategic Placement of Electric Vehicle Charging Stations: Advancing Decarbonisation in the Transport Sector
The global shift toward sustainability aims to drive transformative changes in the transportation sector, particularly by adopting electric vehicles (EVs) as a cleaner alternative to traditional internal combustion engine vehicles. While the adoption of EVs is accelerating worldwide, supported by government incentives, a critical challenge is establishing adequate charging infrastructure to support this transition. Strategic placement of charging stations is vital, requiring careful consideration of factors such as demand, social equity, energy and integration with transportation networks while discouraging excessive reliance on private vehicles. Although prior studies have explored charging infrastructure through separate spatial and mathematical optimisation lenses, a comprehensive approach that incorporates spatial, energy, and sociodemographic factors remains lacking. This project aims to fill this gap by developing a modelling framework that equips planners and policymakers with a robust tool for locating EV charging stations based on defined criteria and available data. The primary goal of this project is to create an optimisation tool integrated with a spatial model to identify optimal locations for EV charging stations within a given region. Key objectives include identifying critical parameters influencing charging station placement, formulating an optimisation problem to maximise utilisation while ensuring equitable access, and designing an interactive tool that allows stakeholders to visualise charging station locations based on regional needs and constraints.
Deadline : 15 March 2026
(14) PhD Degree – Fully Funded
PhD position summary/title: Real time mixing control in flow chemistry
We have recently shown that tuning flow rate, temperature, and residence time can control particle size and morphology during flow crystallisation of fast-forming organic materials (Shaughnessy et al, 2023; Watson PhD thesis 2025). Extending this to control crystallinity is important for producing high-performance materials for selective separation of e.g., hydrogen isotopes (Liu et al, 2019).
However, current systems lack dynamic control over mixing – a critical factor in rapid crystallisation. In many lab flow set-ups, passive mixing occurs via static junctions (e.g. T-junctions) under steady laminar flow, where diffusion dominates. Adjusting mixing parameters requires physical changes to the setup, leading to downtime and limited flexibility.
This PhD project proposes a novel mechatronic system for real-time control of mixing by actively modifying inlet geometry and residence time. A variable-volume reactor will be designed using an actuated periscopic channel to dynamically adjust length and hence residence time. Inlet junctions will be 3D-printed in compliant resin, incorporating magnetically actuated flaps to alter geometry on demand. Numerical simulations will guide the design to optimise flow and mixing efficiency. The final system will be integrated into a commercial flow chemistry platform, offering continuous, fine-grained control over crystallisation reaction conditions. This will be demonstrated through the synthesis of porous materials with controlled particle size, morphology, and crystallinity, and with application in materials synthesis where mixing has influence over material formation (e.g., polymer nanoparticles, hierarchical materials, influencing nucleation/early stages of COF/MOF formation).
Deadline : 31 March 2026
(15) PhD Degree – Fully Funded
PhD position summary/title: High Power Laser Development
The studentship will focus on creating high-energy, high repetition-rate lasers. You will work with optical fibre lasers and combine the output of these systems using polarisation combination to create one output beam. The aim is to harness the advantages of chirped pulsed amplifier Yb lasers over other solid-state systems by using a combination of techniques to increase the energy output. This performance is not possible with the current systems, and so this project involves working at the forefront of laser technology to make a difference.
The main objectives of this project are:
- Investigation of current systems, and how we can improve the performance levels to make a difference in the field.
- Research into optical fibre lasers and how we can combine the output to create one beam using polarisation.
- Development of innovative systems and methods at the forefront of laser technologies.
Deadline : 31 March 2026
(16) PhD Degree – Fully Funded
PhD position summary/title: Develop Cryo-Enabled Multi-Scale 3D Bioprinting for Engineering Shelf-Ready Human Tissues
3D bioprinting is an emerging technology that enables the fabrication of biomimetic engineered tissues, which are crucial for understanding diseases and facilitating tissue regeneration. Among existing methods, ink-extrusion bioprinting and femtosecond laser direct writing (FsLDW) are particularly promising. Ink-extrusion bioprinting enables efficient multi-material printing but is limited to hundreds of micrometres in resolution. FsLDW offers unmatched nanoscale precision and true 3D patterning capacity but is relatively slow and equipment-intensive, and thus less commonly used in bioprinting. Integrating ink extrusion and FsLDW will enable technical complementarity for high-fidelity tissue fabrication, but it remains challenging.
To address the challenge, this project will develop a multi-scale, multi-material biofabrication platform that combines ink-extrusion bioprinting, cryopreservation, and FsLDW to create hierarchically structured, cryo-preservable, and shelf-ready 3D organ models. These constructs will better emulate human organs and enable easy storage and transport, advancing regenerative medicine, disease modelling, and drug discovery.
Deadline : 15 March 2026
(17) PhD Degree – Fully Funded
PhD position summary/title: Combined radiation and corrosion effects in advanced alloys for molten salt nuclear reactors
Reliable, efficient nuclear energy is critical to achieving a net-zero future. Molten Salt Reactors (MSRs) are a promising Generation IV reactor technology in which nuclear fuel is dissolved in a molten salt coolant (FLiNaK). This design offers higher thermal efficiency than traditional Light Water Reactors (LWRs) and enables the use of alternative fissile isotopes. However, a key challenge lies in developing structural materials that can withstand the corrosive, radioactive salt environment while maintaining their mechanical and chemical integrity over time.
Molten salt corrosion is a complex phenomenon involving multiple processes, including the loss of constituent elements from structural alloys (leading to wall thinning) and preferential leaching that modifies surface layers. While using highly purified salts can mitigate some corrosion, real MSR systems undergo continual changes in salt chemistry due to burn-up and radiation effects, which introduces new challenges.
This project, in collaboration with Copenhagen Atomics (CA), will investigate molten salt corrosion in conventional and advanced low activation high entropy alloys under realistic reactor-like conditions. Experimental work will be designed to develop a synergistic understanding of how evolving salt chemistry and radiation affect corrosion behaviour. Corroded specimens will be characterised using advanced chemical, structural, and depth-resolved microstructural techniques to understand corrosion kinetics and atomic-scale modifications. The outcomes will contribute to designing more durable materials, supporting the development of next-generation MSRs.
Deadline : 15 March 2026
(18) PhD Degree – Fully Funded
PhD position summary/title: Advanced Hybrid Materials for Passive Atmospheric Water Harvesting
This PhD is delivered through the dual NTHU–University of Liverpool programme. The first two years will be spent at NTHU (Prof. Chia-Her Lin) and the following two years at Liverpool (Dr Hamid Rajabi and Prof John Bridgeman). This collaboration creates a powerful synergy between NTHU’s top-down expertise in molecular-level material design and synthesis and UoL’s bottom-up strength in system-level engineering, prototyping, and sustainability assessment. This end-to-end approach ensures that the developed materials are not only scientifically novel but also practically viable and environmentally sustainable.
This PhD project addresses water scarcity by developing low-energy atmospheric water-harvesting (AWH) materials that can capture moisture from air and release it using sunlight or other mild heat sources. The work will focus on porous, hygroscopic adsorbents integrated into sustainable composite structures that combine strong water uptake at low-to-moderate relative humidity with practical formability, durability, and reduced environmental footprint. The approach emphasises scalable materials design and responsible manufacturing, supporting resilient water technologies aligned with UN Sustainable Development Goal 9 (industry, innovation and infrastructure).
Deadline : 15 March 2026
(19) PhD Degree – Fully Funded
PhD position summary/title: Safe and Decision Focused Learning for Smart Grid Flexibility Services
Power systems are undergoing a rapid transition toward high penetrations of variable renewable energy sources such as wind and solar. This transition introduces significant challenges in maintaining power grid flexibility and balancing supply and demand in real time. Traditionally, flexibility has been provided by large, centralised generators; however, the growing deployment of smart technologies and distributed energy resources enables residential consumers to play an active role in balancing the grid and supporting a more sustainable energy system. By adjusting their energy consumption or generation at optimal times, through demand-side flexibility, households can reduce peak demand, improve grid efficiency, lower reliance on carbon-intensive generators, and promote greater renewable energy integration and decarbonisation.
However, achieving reliable residential flexibility remains difficult and complex. Human behaviour is uncertain and interactive, while comfort and privacy constraints must be respected. Current flexibility models and control algorithms often overlook these user-centric factors, treating end-users as passive system components. Moreover, existing methods primarily rely on forecasting and optimisation, where uncertainties are penalised economically or lead to network constraint violations. These limitations result in suboptimal and potentially unsafe control decisions that hinder large-scale adoption and slow the transition to sustainable energy systems.
This project proposes to address these challenges by developing a safe, coordinated, and decision-focused machine learning framework that integrates expertise from computer science and electrical engineering. The research will explore safe reinforcement learning to ensure user comfort and system reliability, use game theory to model the interactions between users, and apply graph-based learning to capture relationships among households with shared feeders, tariffs, or behavioural similarities. By embedding these techniques within realistic power system models, the project aims to produce trustworthy, human-aware, and scalable algorithms for residential participation in smart grid flexibility services, bridging the gap between technical efficiency, user acceptance, and environmental sustainability in future energy systems.
Deadline : 15 March 2026
(20) PhD Degree – Fully Funded
PhD position summary/title: Atmospheric Plasma Polymer Coatings and Sensor Integration for Harsh Energy & Defence Environments
Photodiodes and other optoelectronic sensing devices are increasingly required to operate reliably in harsh environments, including energy generation, industrial processing, and defence-related settings. In these applications, devices may be exposed to radiation, thermal cycling, moisture ingress, and corrosive chemical attack, all of which can degrade performance, shorten operational lifetime, and compromise measurement integrity. Conventional passivation and protective coating methods (e.g. high-temperature or vacuum-based deposition) can be difficult to implement on sensitive devices or complex assemblies, and may be unsuitable for low-cost, scalable manufacturing or retrofit deployment.
This PhD project will develop atmospheric, low-temperature plasma polymerisation (LTAPP) as a flexible and deployable route to producing passivation and protective coatings for photodiodes, deposited under ambient conditions. The research will investigate how plasma process parameters and precursor chemistries can be engineered to deliver coatings with enhanced barrier performance, corrosion resistance, radiation tolerance, and mechanical durability, while maintaining optical compatibility and preserving device responsivity. Coating strategies will be assessed for their impact on key photodiode performance metrics including dark current, stability, spectral response, and long-term drift.
Potential application areas include radiation-resilient photodetection, robust optical sensing for industrial monitoring and process control, and protective coatings for photodiode-based instrumentation used in nuclear, aerospace, and defence environments. The student will undertake a multidisciplinary programme of work encompassing plasma process development and diagnostics, surface and materials characterisation (e.g. FTIR, XPS, SEM/AFM), accelerated ageing and irradiation testing, and photodiode performance benchmarking.
Deadline : 31 March 2026
(21) PhD Degree – Fully Funded
PhD position summary/title: Autonomous, On-Demand Manufacture of Polymer Nanomaterials in Continuous-Flow
Engineered polymer nanomaterials are a transformational technology due to their low cost, simplicity, versatility, and ease of functionality. If they are to realise their full potential, strategies to discover, optimize, and manufacture precision polymer nanomaterials with uniform size and shape are needed. Continuous-flow processes represent an elegant solution for conducting polymer synthesis and self-assembly as they are highly modular, can be scaled-out, offer real-time monitoring, and can be autonomously controlled. This project will develop a versatile platform for continuous-flow polymer synthesis and self-assembly. A variety of in-line and on-line analysis techniques will be explored including in-line SAXS/SANS, and the resulting data used to develop an AI-guided, autonomous system capable of producing non-spherical nanomaterials of precise size, shape and dispersity on demand. This ‘micelle machine’ will output target precision nanomaterials from monomer feedstocks by autonomous exploration, optimisation, and scale-up of both synthesis and self-assembly steps. This step-change in throughput will rapidly accelerate the discovery and development of precision polymer nanomaterials, overcoming current limitations for producing important yet difficult to access nanomaterials such as those used in nanomedicine and optoelectronics.
Deadline : 1 October 2026
(22) PhD Degree – Fully Funded
PhD position summary/title: Multimodal Deep Learning for Resilient and Robust Remote Sensing Semantic Segmentation
This project is part of a 4-year Dual PhD degree programme between the National Tsing Hua University (NTHU) in Taiwan and the University of Liverpool in England. Such a project offers a unique global research experience, granting 2 PhD awards from world-leading institutions, enabling international and cultural experience, providing access to large-scale national research facilities, and offering the opportunity to build a worldwide network of contacts.
Sustainable industrial growth and resilient infrastructure are central to global development, yet both depend on accurate and fine-grained environmental and land-use mapping. Conducting such mapping through traditional fieldwork is impractical due to its high cost, time requirements, and limited scalability. To address this, researchers have started to combine remote sensing imagery with artificial intelligence in a task commonly known as semantic segmentation – a powerful technique that allows large-scale, automated mapping at reduced cost and effort. Despite the success, most current approaches for remote sensing semantic segmentation still face major limitations, including: (i) reliance on single-source imagery, which hampers their ability to capture the complex interactions among environmental and industrial factors, especially under rapid urbanization and climate change, (ii) class imbalance, where some land-cover types are underrepresented, and (iii) missing data, which often occur in multimodal applications, leading to information loss.
This PhD project aims to develop a next-generation AI-based framework for fine-grained semantic segmentation that leverages multimodal remote sensing data (such as optical, hyperspectral, radar, thermal imagery, and so on) while ensuring robustness and resilience to class imbalance and missing data through the use of cutting-edge Vision-Language Foundation Models and Multimodal Large Language Models. This pioneering project will be the first to jointly address multimodal fusion, missing data, and class imbalance within a unified semantic segmentation framework for remote sensing, thus bringing such a relevant task significantly closer to the real world, where such challenges are extremely common, enabling resilient multimodal fusion under realistic data constraints.
Deadline : 15 March 2026
(23) PhD Degree – Fully Funded
PhD position summary/title: Structure Function Relationship in Multicomponent Biopolymer Encapsulation Materials
Encapsulation materials (microcapsules) offer an attractive “control-release” technology for the dispersion of small volatile substances with scent that breaks mechanically in contact with a physical input such as adsorption on fabrics. This encapsulation approach introduces a protective shell around the cargo (i.e., fragrance) to form a capsule the shell acting as a diffusion barrier enhancing the retention of the cargo. However, the current understanding of the core-shell structure, specifically with respect to the permeation of the cargo, the surface chemistry and the interaction between the various components, is currently hampered by the lack of non-invasive method that allows detailed atomic-scale understanding of all constituents of these multicomponent, multiphase complex materials.
This studentship will allow a highly motivated candidate to develop and exploit NMR approaches supported by complementary techniques, to deliver new chemical understanding capabilities in encapsulated materials. The successful applicant will join an international and multidisciplinary research team that will provide complete student training, skills and development, ensuring strong employability, supported by an industrial research environment. The project is based in the Department of Chemistry at the University of Liverpool, which is an international centre of excellence for the chemistry of advanced materials, and has ample opportunities to secondments with Unilever at the Materials Innovation Factory to gain hands-on experience/appreciation with facilities in an industrial setting. The successful applicant will also have access to state-of-the-art local NMR facilities operating at up to 18.8 T (800 MHz 1H frequency), be able to perform experiments at world-leading large scale NMR research facilities including at the UK High-Field Solid-State NMR Facility, and expand their research vision and interest by attending (inter)national conferences.
Deadline : 24 February 2026
(24) PhD Degree – Fully Funded
PhD position summary/title: Putting a (Better) Brain in the Mobile Robotic Chemist
The overall goal of this project, co-funded by a financial gift from Google, is to create a delocalised global ‘Hive Mind’ that directs autonomous laboratory robots to discover engineered porous materials for atmospheric CO2 capture. We will fuse human insight and AI agents with experimental and computational data streams in real-time, closed-loop robotic experiments to build a new paradigm for tackling complex societal challenges. This studentship will focus on the development of “chemically-aware” agentic AI methodology that can orchestrate autonomous discovery, acting as the ‘brain’ for the robot chemist.
Deadline : 31 March 2026
(25) PhD Degree – Fully Funded
PhD position summary/title: Precision Dual-Atom Catalysts for Carbon–Nitrogen Coupling and Sustainable e-Urea Synthesis
This project is part of a 4 year Dual PhD degree programme between the National Tsing Hua University (NTHU) in Taiwan and the University of Liverpool in England. As Part of the NTHU-UoL Dual PhD Award students are in the unique position of being able to gain 2 PhD awards at the end of their degree from two internationally recognised world leading Universities. As well as benefiting from a rich cultural experience, Students can draw on large scale national facilities of both countries and create a worldwide network of contacts across 2 continents.
Deadline : 15 March 2026
(26) PhD Degree – Fully Funded
PhD position summary/title: New Magnetic Particle Imaging (MPI) methods for the detection of functional changes in diagnostic nanocomposite particles
Research objectives
You will work on an integrated programme that includes:
- Synthesis of magnetic nanoparticles with controlled size across relevant regimes (single cores to clusters, ~5–1000 nm), composition, and shape using co-precipitation and thermal decomposition methods.
- Smart tracers through surface functionalisation, including responsive polymer shells using amphiphilic copolymers and gel matrices designed to alter nanoparticle interaction, aggregation and rotational dynamics in response to biological stimuli.
- Formation of bio-functional nanocomposites, where changes in particle spacing, aggregation, or rotational freedom encode information about chemical or mechanical changes arising from variation in biological environment
- Magnetic characterisation and imaging, using AC susceptometry and Magnetic Particle Imaging to extract information on nanoparticle motion, relaxation behaviour, spatial distribution, and functional response.
Deadline : 28 February 2026
(27) PhD Degree – Fully Funded
PhD position summary/title: Multifunctional Hybrid Glass–TMDC Composites for Environmental Sensing
Sensors that respond to environmental changes – such as gases, light, or humidity – are vital for applications ranging from air quality monitoring to smart infrastructure. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDCs) – MoS₂, MoSe₂, WS₂ – are promising candidates due to their high surface reactivity, tuneable electronic properties [Sensors and Actuators A, 2020, 303, 111875], and ease of integration into devices. However, their sensing performance can be limited by poor selectivity and environmental stability, which necessitates surface modifications such as coatings or hybridisation with other materials [Adv. Funct. Mater. 2022, 32, 2207265].
This project explores the integration of TMDCs with hybrid glasses formed from hybrid organic–inorganic perovskites (HOIPs) and metal–organic frameworks (MOFs) [Adv. Eng. Mater., 2025, 27, 2402554], to create multifunctional composite materials. These hybrid glasses retain the chemical tuneability of their crystalline counterparts while offering unique mechanical and optical properties. By combining them with TMDCs, we aim to engineer interfaces that enhance not only gas sensing but also humidity-sensitive and photo-responsive behaviour.
This project will focus on hybrid glass–TMDC composites for multifunctional sensing, investigating their electronic, optoelectronic, structural and adsorptive properties. Through sustainable synthesis, advanced characterisation, and performance testing, we aim to develop scalable materials responsive to gases, humidity, and light. By combining experimental and computational approaches, the project supports the development of adaptable, high-performance sensors suitable for integration into environmental monitoring systems, flexible electronics, and smart infrastructure.
Deadline : 15 March 2026
(28) PhD Degree – Fully Funded
PhD position summary/title: Molecular Modelling and Data-Driven Discovery of Sustainable Home-Care Products
Predicting optimal compositions in home-care products is a major challenge, given the vast design space of often high-value ingredients. In detergents, this requires understanding the complex aqueous chemistry of small molecular components (e.g., fragrance molecules) and how they interact with fibrous materials. Exploring this empirically for the thousands of components available to formulation design would be resource-intensive and impractical. This motivates the PhD project, which will exploit a combined data science–molecular simulation approach, cross-validated with wet chemistry, to optimise existing formulation products and, when combined with state-of-the-art cheminformatics approaches, design new ingredients for sustainable product innovation.
You will employ Bayesian optimisation to systematically reduce large catalogues of potential formulation ingredients based on key descriptors derived from existing data. For the selected subset, enhanced-sampling molecular dynamics approaches will be used to determine mechanisms and rates for the reversible binding of molecules to fibrous surfaces in wet and dry conditions—the key indicators of ingredient performance. The simulations will reveal how molecular topology and chemistry control penetration of the surfactant-rich interfacial layer at fibres during washing, and subsequent molecule release in air.
By integrating these molecular insights with Bayesian inference and cheminformatics, the computational tools developed in this project will enable the efficient selection and prediction of new formulation ingredients for direct evaluation in wet chemistry experiments carried out by industry partners. As such, we are seeking a highly motivated candidate with interests in molecular modelling, digital design for real-world problems, and combining advanced tools in data science with complex molecular-scale problems.
Deadline :
(29) PhD Degree – Fully Funded
PhD position summary/title: Generative AI Models for Materials Discovery
You will explore cutting-edge techniques in generative modelling (e.g., diffusion models and large language models) and integrate them with chemically-informed constraints and first-principles calculations. The goal is to contribute to AI-driven improvements of the crystal prediction workflow to generate experimental targets, predict their stability and properties, and ultimately accelerate materials discovery beyond current paradigms.
You will join a multidisciplinary research group working at the interface of solid state materials science and AI. You will have access to high-performance computing resources, work closely with experimentalists, and have the opportunity to publish in leading journals. This studentship is suited for a student with a background in computational materials science, machine learning or artificial intelligence. Experience with Python and writing code is essential. Experience with ML frameworks (PyTorch/TensorFlow), graph and/or neural nets and familiarity with materials science, crystallography and/or solid-state chemistry would be an asset. Please clearly highlight your relevant experience in your application.
Deadline : 31 August 2026
(30) PhD Degree – Fully Funded
PhD position summary/title: Discovery of new inorganic materials for net zero applications
The experimental discovery of new inorganic materials shows us how crystal structure and chemical composition control physical and chemical properties. It is therefore critical for our ability to design functional materials with the properties we will need for the next zero transition. Examples include ion motion and redox chemistry in batteries for transport and grid storage, solar absorbers for photovoltaic technologies, rare-earth-free magnets for wind power, catalysts for biomass conversion or water splitting for hydrogen generation, components in low-energy information technology and myriad other unmet needs.
This PhD project will tackle the synthesis in the laboratory of inorganic materials with unique structures that will expand our understanding of how atoms can be arranged in solids. The selection of experimental targets will be informed by artificial intelligence and computational assessment of candidates, working with a multidisciplinary team of researchers to maximise the rate of materials discovery. The resulting materials will be experimentally studied to assess their suitability in a range of applications, including targeting Li and Mg transport for advanced solid state battery materials. The student will thus both develop a strong materials synthesis, structural characterisation and measurement skillset, and the ability to work with colleagues across disciplines in a research team using state-of-the-art materials design methodology. The success of this approach is demonstrated in a range of papers (Science, 2024, 383, 739-745; J. Am. Chem. Soc., 2022, 144, 22178-22192; Science, 2021, 373, 1017-1022).
Deadline : 31 July 2026
(31) PhD Degree – Fully Funded
PhD position summary/title: Discovery of inorganic cathode materials and/or solid electrolytes for next generation battery technology
This project focuses on the discovery of next generation battery materials through experimental design and compositional exploration. Combining solid-state synthesis, advanced structural characterization, and electrochemical optimization the project will explore novel cathode materials and/or solid electrolytes and offers an opportunity to develop expertise in materials chemistry while collaborating with computational scientists, physicists, and engineers to accelerate clean energy innovation.Rechargeable batteries play a critical role in enabling the global transition towards clean and sustainable energy technologies. Discovery of new high-performance cathode materials and solid electrolytes is the core challenge to advance these technologies. This project involves the experimental design and compositional exploration of a new class of inorganic materials, detailed characterisation of the materials and full-cell level optimisation of the electrochemical properties and understanding of relevant new mechanisms and chemistries.
The project will combine synthetic solid-state chemistry, advanced structural analysis and measurement of physical and electrochemical properties of new cathode materials and solid electrolytes, enabling the successful candidate to develop a diverse experimental skillset in materials chemistry and battery chemistry. The focus will be on the discovery of new materials and structures with enhanced performance, accelerated by working with computational design experts. Owing to the multi-faceted nature of this dynamic project, the student will work closely with computer scientists, inorganic (electro)chemists, physicists, engineers, and material scientists to discover new inorganic cathode materials and solid electrolytes for batteries. This provides an opportunity to a participate in AI-driven discovery.
Deadline : 31 August 2026
(32) PhD Degree – Fully Funded
PhD position summary/title: Discovering CO2-capture materials using robots and ‘Hive Mind’ hybrid intelligence
The goal of this project, co-funded by a financial gift from Google, is to create a delocalised global ‘Hive Mind’ that directs autonomous laboratory robots to discover engineered porous materials for atmospheric CO2 capture. We will fuse human insight and AI agents with experimental and computational data streams in real-time, closed-loop robotic experiments to build a new paradigm for tackling complex societal challenges. You will develop skills in laboratory work, automation, AI and programming. This studentship will focus on the development of a modular automation platform for synthesis and characterisation of porous materials. You develop skills in laboratory work, robotics and automation, and work closely with AI scientists to integrate this automation platform with an “AI brain” to perform end-to-end autonomous materials discovery and synthesis.
Deadline : 31 March 2026
(33) PhD Degree – Fully Funded
PhD position summary/title: Chemospintronics – using in-situ spectroscopy to study the breaking of scaling relationships in catalysis with spintronics materials
Scaling relationships exist across catalysis as the binding energies of surface intermediates are typically interrelated – optimisation of the catalyst structure to achieve a change in the binding energy of one intermediate will lead to a change in the binding energy of the other species along the reaction pathway. This limits the degrees of freedom available within catalyst design, placing an apparent upper-limit in achievable catalytic activity, which can be visualised in the form of the “peak of the volcano” in a 2D catalytic activity-descriptor plot. Recently numerous reports of electrocatalysis at ferromagnetic electrodes have shown dramatic changes in activity for reactions including hydrogen and oxygen evolution from water (Nature Catalysis, 2019, 2, 971–976). Activity changes are often claimed to arise due to the use of a spin-polarized surface, which could lead to a breaking of the scaling relationships due to spin effects on adsorption energies that selective (de)stabilise reaction species.
This is exciting but the approach appears reliant on the use of ferromagnetic materials, limiting applicability. Very recently (J. Am. Chem. Soc. 2026, 148, 1, 967–975) we showed in a proof-of-concept project that dramatic changes in electrocatalytic activity can also be achieved using originally non-magnetic catalysts grown on multilayer magnetic structures. We proposed that the large change in catalytic activity was due to induced spin-polarization in the catalytic layer leading to a change in the adsorption energies of reaction species. This Chemospintronic approach, where spintronic structures are used to modify the chemical catalytic activity of existing materials, has the potential to circumvent scaling relationships across photo-, thermal-, and electrocatalysis and transform our design of catalysts. This could unlock new catalytic materials that have activities vastly exceeding the current state-of-the-art. But whilst the catalytic effects are clearly evidenced in our initial study, the hypothesised mechanisms by which they operate are not.
Deadline : 11 April 2026
(34) PhD Degree – Fully Funded
PhD position summary/title: Automated virtual and physical screening of molecules for application in optoelectronics devices.
High-throughput virtual screening of conjugated molecules is a mature area with reliable computational datasets approaching millions of compounds and experimental validations for thousands of them. However, all applications in optoelectronic devices require molecular materials with optimal photophysical properties (lifetimes, fluorescence yields, rates of singlet-fission, up-conversion, oxidative and reductive quenching, etc.). These are currently not predictable by high-throughput computational methods. Furthermore, experimental data of photophysical properties are limited and inhomogeneous. The two key objectives of this combined theoretical/experimental problem are:
- To expand the capabilities of virtual screening for photophysical properties for datasets of the order of hundreds of thousands of entries.
- To exploit automated optical time-resolved characterization methods to construct reliable and homogeneous datasets of thousands of entries.
The two objectives are interdependent because reliable experimental datasets in (2) are required to fine tune many aspects of the methodology to be developed in (1). The challenge of the second objective is the development of automated interpretation of the optical spectra (absorption, excitation, fluorescence and fluorescence lifetime) which is now performed manually for just a few systems at a time.
Deadline : 31 March 2026
About The University of Liverpool, England –Official Website
The University of Liverpool (abbreviated UOL; locally known as The Uni of) is a public research university in Liverpool, England. Founded as a college in 1881, it gained its Royal Charter in 1903 with the ability to award degrees, and is also known to be one of the six ‘red brick’ civic universities, the first to be referred to as The Original Red Brick. It comprises three faculties organised into 35 departments and schools. It is a founding member of the Russell Group, the N8 Group for research collaboration and the university management school is triple crown accredited.
Ten Nobel Prize winners are amongst its alumni and past faculty and the university offers more than 230 first degree courses across 103 subjects. Its alumni include the CEOs of GlobalFoundries, ARM Holdings, Tesco, Motorola and The Coca-Cola Company. It was the UK’s first university to establish departments in oceanography, civic design, architecture, and biochemistry (at the Johnston Laboratories). In 2006 the university became the first in the UK to establish an independent university in China, Xi’an Jiaotong-Liverpool University, making it the world’s first Sino-British university. For 2021–22, Liverpool had a turnover of £612.6 million, including £113.6 million from research grants and contracts. It has the seventh-largest endowment of any university in England. Graduates of the university are styled with the post-nominal letters Lpool, to indicate the institution.
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