Funding Opportunities

Mary Margaret Wooloff Scholarship

Margaret Wooloff was an active member of the Old Students’ Association (OSA).  She graduated from Aberystwyth University with a degree in French in 1942 and a teaching qualification in 1943. After teaching in post-war Paris, she embarked on a prestigious career in teaching, and was the former headmistress of Queen Elizabeth Grammar School for Girls and Queen Elizabeth Cambria School, Carmarthen (1960-1983).

Margaret Wooloff PhD Scholarship - Details of the Award & Available Projects

Open to applicants who qualify for Home (UK) fees status only, there are three full-time PhD scholarships available.  These will be allocated one per Faculty and on a competitive basis to three of the projects described in the Margaret Wooloff PhD Scholarship 2023 Project Details. Those awarded a Margaret Wooloff Scholarship will receive a grant for up to three years which will cover their tuition fees up to the UK rate of £4,712 per annum (2023/24 rate).  A maintenance allowance of approximately £18,622 per annum* and access to a travel and conference fund (max. £500 per annum*) will also be provided. Scholarships commence in September 2023 (although flexible starts up to February 2024 can be discussed.)

How to Apply

Deadline 27th September 2023

To be considered, candidates must complete the usual full online PhD application AND the specific Margaret Wooloff PhD Scholarship Application Form 2023

The completed Margaret Wooloff Scholarship Application Form should be submitted via our online Postgraduate Application Portal at the point of application.   

To make a full PhD application, firstly visit our course pages and find the details of the course for which you wish to apply.  Once you have found your chosen course page, select the “Apply Now” button to start your application.  

The Postgraduate Admissions Application Portal will ask you to provide us with your personal details, confirm your course selection(s) and upload documents in support of your application.  Please have you supporting documents saved in PDF format and ready to upload to your online application. 

At the same time, the completed Margaret Wooloff Scholarship Application Form should also be sent as an attachment by email to Prof Reyer Zwiggelaar (rrz@aber.ac.uk), Head of Graduate School, with the subject heading MARGARET WOOLOFF SCHOLARSHIP APPLICATION. 

Please ensure that you read the Margaret Wooloff PhD Scholarship Terms & Conditions  thoroughly. 

Any Questions?

If you have any specific queries regarding the projects listed, please contact the main supervisor associated with the project.  

If you have any queries about the postgraduate application process please contact pg-admissions@aber.ac.uk 

Margaret Wooloff PhD Scholarship 2023 Project Details

Chromosome Paints: Investigating Chromosome Pairing in Arabidopsis using Oligo Paints

Dr Andrew Lloyd (IBERS) - anl50@aber.ac.uk 

Meiosis, a specialised cell division, underlies sexual reproduction. During meiosis pairs of related chromosomes (homologs) must find one another, co-align, and undergo reciprocal exchange known as “crossing-over”. Finding and crossing-over with the correct partner chromosome is critical for fertility and genome stability. While meiosis has a century long history of study, we still know relatively little about how chromosomes find the right partner and restrict crossing-over to true homologs.

A major challenge in studying the homology search process is being able to track specific chromosome combinations during meiosis. Recently, Oligo Painting has emerged as a powerful and versatile tool for labelling specific chromosomes or chromosomal regions using Fluorescence In Situ Hybridisation (FISH) microscopy. This PhD project will, in collaboration with US partners, develop a high-density genome-wide Oligo Pool (~600,000 oligos) for Oligo Painting in the model plant genus Arabidopsis. Using this tool, we will for the first time, be able to specifically label any chromosome, chromosomal region, or combination thereof by fluorescence microscopy. You will use this tool to explore exciting new research areas addressing questions such as “how do chromosomes find the right partner in meiosis?” and “are crossover hot-spots the first regions to associate with recombination proteins?”.

This PhD will provide training in super-resolution microscopy, molecular biology, bioinformatics, and other transferable skills as well as providing opportunity for research placements with international collaborators. You will also be helping develop a new and important tool for the wider Arabidopsis research community, opening many avenues for future collaboration.

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Investigating Area Selective Deposition (ASD) as an Enabler for Future Technologies

Dr Anita Brady-Boyd (Department of Physics) - anb116@aber.ac.uk 

As feature sizes continue to decrease in integrated circuit chips, fabricating these devices becomes increasingly difficult. At present Apple’s most current chip boasts an incredibly small 28 nm width for their interconnects, which are the wires connecting the individual transistors. The transistors themselves are ~ 48 nm wide. Area selective deposition (ASD) allows for the nanoscale patterning of materials while limiting the use of traditional photolithographic and etch steps which can be time-consuming, expensive, and wasteful of materials. Although only a new field of research, ASD has been hailed as a driver for next generation technology. This PhD will take a fully interdisciplinary approach to answer fundamental questions about ASD with the core objective of the project to progress towards implementing ASD processes into nanoelectronics fabrication. ASD requires the use of small molecules called self-assembled monolayers (SAMs). These molecules create both a physical and chemical barrier to block deposition. The initial stages of the project will involve fundamental study into how these molecules interact and adhere to industry relevant substrates. Different methods of deposition for the SAMs will be investigated. Next, the ability of the SAM to block any subsequent deposited material will be investigated. This will utilise a lot of the world-class equipment already available to the Physics department. The final part of this project will involve collaboration with the industrial links of the PI. This stage involves the scale up of our ASD process to high volume manufacturing on 300 mm wafers used in industry.

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Discourses of Celticity on the Far-right: Authenticity, Appropriation and Antagonism

Dr Ben O Ceallaigh (Department of Welsh and Celtic Studies) - beo15@aber.ac.uk 

The growth of the far-right has been a notable political development internationally in recent years. Often understood as a response to the socio-economic precarity and austerity measures that have characterised many economies since 2008 (Blyth and Matthijs 2017: 218-219), such nativist and fascist movements frequently invoke visions of an idealised past which they see as offering a blueprint for the good society (Eco 1995). While more commonly drawing inspiration from Norse and Classical antiquity (Dahmer 2019), a minority current within fascist syncretism also looks to the languages and cultures of the six Celtic nations, presenting them as paragons of the racial purity and traditionalism that are key ideological pillars of fascism (Eco 1995; Wilson 2022). Indeed, the symbol of the largest white supremacist website is a stylised version of the Celtic cross, and several currently active neo-Nazi groups have made explicit use of Celtic languages and folklore in their propaganda.

This PhD will explore the various ways that Celticity has been instrumentalised by those on the far-right, examining historical and contemporary contexts. With movements for the promotion of the Celtic languages and their associated cultures typically being pluralist and left-leaning (Alessio 2015: 298), the challenges that fascist appropriation offers these movements will be interrogated with the goal of assessing how such difficulties can be best countered.

The project will be supervised by Dr. Ben Ó Ceallaigh of the Department of Welsh and Celtic Studies, who has a background in Celtic language revitalisation, sociology and political science, as well as a long-standing interest in fascism and anti-fascism in Ireland, Britain and beyond.

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Substate Nations and their Responses to People Seeking Sanctuary and Refugees: An International Comparison

Dr Catrin Wyn Edwards (Department of International Politics) - cwe6@aber.ac.uk 

In recent years, sub-state nations have become increasingly involved in the governance of refugees and asylum seekers (Bernhardt 2022; Edwards & Wisthaler 2023). In April 2019, the Welsh government declared its intention to become ‘a true ‘Nation of Sanctuary’ (Welsh Government 2019) while Scotland extends social rights to refugees and asylum seekers (Mulvey 2017). This has added to an already complex landscape of cooperation and contestation between international nongovernmental and governmental organisations, state and local governments, third sector, voluntary, and religious organisations, and private companies (Campomori & Ambrosini 2020, Guiraudon & Lahav 2000; Van der Leun 2006; Scholten 2011). By adopting a comparative approach to three substate nations, Wales, the Basque Country and Flanders, this project will explore the multilevel and horizontal dynamics of migration governance beyond and below the state, and will identify whether (and if so, how and why) they frame their responses as a means to demarcate themselves from the state, whether substate responses to the refugee ‘crisis’ have challenged the central state in its own normative and empirical space and if their approaches are linked to questions of belonging and identity, which are crucial for some sub-state nations. In focussing on a hitherto under-researched subject of sub-state responses to refugees and people seeking asylum, the project offers a theoretical and empirical contribution to the field of minority nationalism studies and multi-level governance that is distinct from the current scholarship.

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Exploring the future of pasture and forage nutrition for the equine.

Dr Elizabeth Hart (Department of Life Sciences) - elh18@aber.ac.uk 

It has recently been shown by Hart et al., (2022) that potential climate change scenarios can affect the feed quality of forage grasses for ruminants. With further implications of climate change on crop yields and the rising cost of compound feeds the role of forages within non ruminant herbivores, such as equines, will become more dominant. Horses are hind gut fermenters allowing them to utilise fibre through the synergistic relationship with the hind gut microbiome which produces energy yielding volatile fatty acids. There is a delicate balance between a healthy and dysbiotic microbiome which can be changed by various feed components such as non-structural carbohydrates. Forages alter their nutritive/chemical characteristics in relation to external stresses such as climate change. These stressed forages may affect the fermentation profile and microbiome within the hind gut leading to potential metabolic disorders. This project aims to evaluate the impact of climate change scenarios on the fermentation characteristics of the equine hind gut and overall feed digestibility. Initially screening will focus on different native forages grown under controlled climate scenarios on chemical composition, in vitro degradability, fermentation profiles and microbial community analysis. Secondary studies will use a continuous culture technique to assess 3 treatments in greater detail prior to in vivo studies examining apparent whole-tract digestibility.

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Language Learning in Later Life: Psychological and Wellbeing Benefits

Dr Hanna Binks (Department of Psychology) - hlb13@aber.ac.uk 

This project explores the cognitive, psychological and wellbeing benefits of learning Welsh in older adults. In bilinguals, the ability to switch between languages is important from a cognitive perspective, with research suggesting bilinguals demonstrate slower cognitive decline in comparison to monolinguals. Switching from a dominant language to a weaker language places greater demand on the attention shifting capacities of the speakers. As this switching is more effortful for beginners, the cognitive benefits pre- and post- initial language learning should be largest for learners, with benefits typically observed in terms of executive function and cognitive control. This project aims to extend this work into language learning in later life, where the cognitive gains may be particularly important.

The project has three main objectives:

1. Determine whether learning a language in later life results in improved cognitive functions from baseline.

2. Investigate whether participating in language learning in later life results in improved wellbeing.

3. Develop and adapt language learning materials specifically for older adults.

The project develops the interdisciplinary research themes that exist between the two supervisors Dr Hanna Binks and Dr Victoria Wright. The ideal PhD candidate will have a background in psychology, linguistics, or a related discipline, and will have a good understanding of statistical analysis and quantitative research methods.

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Noisy Measurements in Quantum Information Theory

Dr Jukka Kiukas (Department of Mathematics) - jek20@aber.ac.uk 

This project will develop the theory of noisy quantum measurement processes, focusing on aspects of (in)compatibility between the noise and measurements motivated by applications to quantum information processing.

Incompatibility - the existence of measurements and transformations which cannot be realised simultaneously on a physical system - is motivated by the foundations of quantum theory, as well as the current development of practical quantum devices. While the foundations reach back to Heisenberg’s Uncertainty Principle, the current focus is on modelling quantum phenomena in the presence of noise. On the one hand, incompatibility between measurements is one such phenomenon, crucial e.g. for cryptography and state discrimination. On the other hand, incompatibility between the process transformations and available measurements limits the implementation. The two aspects are intricately linked, both reflecting the quantum character of measurements.

The overall aim is to derive analytical conditions for incompatibility for processes motivated by open quantum systems. Analytical methods will be supported by numerical convex optimisation. One starting point is pure decoherence, for which the problem has proved tractable even for certain large systems. The topic has a broad intersection with active research areas on quantum contextuality, resource theories, correlations, steering, and monitoring of open systems, providing plenty of challenging directions to explore.

The candidate should have an undergraduate degree in Mathematics or Physics, with grade II(1) or above. Interest in the mathematical structure of quantum theory is essential. Master’s degree and/or knowledge on quantum information theory / open systems is desirable.

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Surveillance and Disability: Control, Discrimination, and Resistance in Fin-de-Siècle Literature

Dr Lucy Thompson (Department of English & Creative Writing) - let22@aber.ac.uk 

The project aims to investigate the underexplored issues of surveillance practices on the lives of disabled individuals during the Fin de Siècle, specifically addressing their experiences of

ableism, discipline, and marginalisation. By reassessing how surveillance methods were employed in ways that disadvantaged disabled people, as well as the ways they were contested, we hope to enhance our understanding of inclusivity and equity in the present day.

The project will require a deep and nuanced understanding of the complex intersections between surveillance, disability, and literature. Moreover, it demands a robust research methodology, encompassing critical analysis of literary works, archival research, and engagement with interdisciplinary fields such as Critical Disability Studies and Surveillance Studies.

Potential research questions to be addressed include:

· How do the surveillance practices portrayed in fin-de-siècle literature intersect with disability and contribute to the marginalisation of disabled individuals?

· In what ways do the experiences of disabled characters in fin-de-siècle literature challenge prevailing narratives of surveillance, power, and control?

· What strategies and tactics did disabled individuals use to resist and contest surveillance practices, and how do these practices intersect with broader social movements for disability rights during the Fin de Siècle?

Applicants will utilise a historicist approach to examine the interplay between historical context and literary form, as well as incorporate archival research to shed light on potentially overlooked materials. The proposal can be adapted based on the applicant's preferred genre of literature.

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Cannabinoids to Promote Wound Healing in an Equine In Vitro Model

Dr Ruth Wonfor (IBERS) - rec21@aber.ac.uk 

Equine wound healing is problematic and frequently with complications, such as chronic inflammation, impaired wound contraction and granulation tissue overgrowth, preventing complete healing of the wound. Furthermore, evidence from human medicine demonstrates that the presence of biofilms can further exacerbate chronic wound healing.

Cannabinoids are compounds derived from the plant Cannabis sativa, which work through the endocannabinoid system and have numerous medicinal benefits, including anti-inflammatory, anti-microbial and wound healing. Thus, cannabinoids have the potential to be used as a wound healing therapy for chronic, poor healing wounds, such as those found in horses.

The aim of the project is to assess the role of cannabinoid compounds in an equine in vitro wound healing model, focussing on both the fibroblast cellular repair response and the actions on common pathogenic skin microbes. The project will be completed through 3 main objectives:

1. Assessment of the effect of cannabinoids in an in vitro cell culture model of wound healing in horses, by assessment of wound contraction, cell viability and inflammatory markers.

2. Understanding the mechanism of action of cannabinoids on wound healing by knock down of relevant pathways.

3. Assessment of the antimicrobial activity of cannabinoids on common skin pathogens and biofilms by bactieral growth curves and consortia of skin pathogens and biofilm models.

The student will be trained in mammalian cell and microbe culture methodologies, as well as assays to test proliferation, protein and genetic markers and will gain industrial insights from a leading cannabinoid company, TTS Pharma, who will supply cannabinoids.

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Transition from a Metaverse Smart City to Multiverse Smart Cities using Machine Learning in Intelligent Transportation System

Dr Yasir Saleem (Department of Computer Science) - yss1@aber.ac.uk 

Smart cities are equipped with various types of sensors related to intelligent transportation

system (ITS) that provide measurement of different parameters. The measured data are made publicly available for researchers to analyse and infer useful information. However, such datasets contain inconsistencies (such as outliers and missing values) which require data pre-processing before data analysis and inference. Machine Learning (ML) techniques can be used in data analysis to infer meaningful information. It is interesting to develop a metaverse of smart city in ITS by analysing how one parameter can be predicted by using other parameters.

The models created for a metaverse of smart city can be generalized to be directly applied to

the multiverse of smart cities that will help to save time and efforts while analysing other

smart cities datasets.

The aim of this PhD studentship is to develop an automated tool for data pre-processing

of smart cities in ITS, perform data analysis, develop prediction models, and apply

the model to other smart cities.

There are three main objectives of this PhD studentship as follows:

1. To develop an automated data pre-processing tool using ML for smart cities’ datasets that can deal with common issues of data pre-processing and be able to handle high dimensional and heterogeneous data.

2. To develop a metaverse of smart city in ITS by performing data analysis using ML.

3. To develop a generalized model for a metaverse of smart city that can be directly applied to multiverse of smart cities in ITS.

WGSSS Studentships

Aberystwyth University is a member of the ESRC Welsh Graduate School in Social Science (WGSSS) which supports a number of fully-funded studentships in the social sciences. Students can apply for studentships in four research areas or ‘pathways’: (1) Environmental Planning, (2) Health, Well-Being and Data Science, (3) Human Geography and (4) Politics, International Relations, and Area Studies. Information on each pathway is provided in the links below.  

Applicants should consider approaching potential supervisors before submitting their application to confirm that there is appropriate supervisory capacity and to discuss their draft application.

What will the studentship cover:

Studentship awards cover your tuition fees as well as a maintenance grant and include access to additional funding through Research Training Support Grants (RTSG).  There are other opportunities and benefits available to studentship holders, including an overseas fieldwork allowance (if applicable), internship opportunities, overseas institutional visits and other small grants.

Eligibility

WGSSS studentships are highly competitive. Applications should come from exceptional candidates with a first class or strong upper second-class honours degree, or appropriate Master’s degree. The University values diversity and equality at all levels and encourages applications from all sections of the community, irrespective of age, disability, sex, gender identity, marital or civil partnership status, pregnancy or maternity, race, religion or belief and sexual orientation.  In line with our commitment to supporting and promoting equality, diversity and inclusion, and to increase recruitment of currently underrepresented groups, applications from Black British, Asian British, minority ethnicity British and mixed-race British candidates are particularly encouraged and welcomed.  We welcome applications for both full and part-time study.

Pathways:

Detailed information on eligibility, topic areas and the application process can be found on the links below.

Environmental Planning

Health Wellbeing and Data Science

Human Geography

Politics, International Relations, and Area Studies

 

The Welsh Graduate School for the Social Sciences (WGSSS) recently hosted two webinars; ‘How to apply for a WGSSS studentship’ and ‘How to write a research proposal’. The webinars were designed to make the competition more accessible to those who are considering applying in the 2024 WGSSS studentship competition. The webinars covered topics such as; how to find a supervisor, how to prepare for an interview, and how to structure your proposal. Recordings of the webinars are available on the WGSSS studentships page.

 

WGSSS welcomes applications from students of all backgrounds. We value academic excellence and life skills, as well as the ability to meet challenges and student’s capacity to enrich the life of our community. Widening participation is a key goal for WGSSS and we are keen to receive applications from able and ambitious students. We are a collaboration between Cardiff University (the lead institution), Aberystwyth University, Bangor University, Cardiff Metropolitan University, the University of Gloucestershire, the University of South Wales and Swansea University.  

 

The closing date for applications in the General Competition is the 12th January 2024 (institutions may have earlier deadlines, these will be detailed in the individual General Competition adverts), the Collaborative Competition will be launching in March 2024.

 

Isabel Ann Robertson Scholarship

Isabel Ann Robertson, always known as Ann, was a tutor in the Computer Science Department at Aberystwyth University for 25 years from 1984 to 2009. But her links with the University spanned several generations. Ann Davies was born in London in 1932, the eldest of three children. Her mother, Enid Sayers, had graduated in English from the then University College of Wales in Aberystwyth in the 1920s and later (as Enid Davies) was Vice President of the Old Students’ Association. Ann’s father, C W Davies, was also an Aber graduate and was later a professor of Chemistry and Head of the Chemistry Department. Ann studied Physics when the department was still based in the Old College on the seafront, graduating with a BSc in 1954 and an MSc by research in 1957. Her research was on cavitation. She was also a College athlete and a member of the Sailing Club. In 1956 she married David Robertson, whom she had met through the Sailing Club. His work for the Forestry Commission took them to many different parts of the UK, including Glasgow, where Ann took an MSc in Computer Science. They returned to Aberystwyth to live in the 1980s. Their daughter, Sara Robertson, also studied at Aberystwyth from 1978 to 1981 and their granddaughter, Fiona Robertson, followed, from 2011 to 2015.

Ann Robertson PhD Scholarship - Details of the Award & Available Projects

Open to applicants who qualify for Home (UK) fees status only, there are three full-time PhD scholarships available.  These will be allocated one per Faculty and on a competitive basis to three of the projects described in the Ann Robertson PhD Scholarship 2023 Project Details. Those awarded an Ann Robertson Scholarship will receive a grant for up to three years which will cover their tuition fees up to the UK rate of £4,712 per annum (2023/24 rate).  A maintenance allowance of approximately £18,622 per annum* and access to a travel and conference fund (max. £500 per annum*) will also be provided. Scholarships commence in September 2023 (although flexible starts up to February 2024 can be discussed).

How to Apply 

Closing date 27th September 2023

To be considered, candidates must complete the usual full online PhD application AND the specific  Ann Robertson PhD Scholarship Application Form 2023 

The completed Ann Robertson Scholarship Application Form should be submitted via our online Postgraduate Application Portal at the point of application.   

To make a full PhD application, firstly visit our course pages and find the details of the course for which you wish to apply.  Once you have found your chosen course page, select the “Apply Now” button to start your application.  

The Postgraduate Admissions Application Portal will ask you to provide us with your personal details, confirm your course selection(s) and upload documents in support of your application.  Please have you supporting documents saved in PDF format and ready to upload to your online application. 

At the same time, the completed Ann Robertson Scholarship Application Form should also be sent as an attachment by email to Prof Reyer Zwiggelaar (rrz@aber.ac.uk), Head of Graduate School, with the subject heading ANN ROBERTSON SCHOLARSHIP APPLICATION. 

Please ensure that you read the Ann Robertson PhD Scholarship Terms & Conditions thoroughly. 

Any Questions? 

If you have any specific queries regarding the projects listed, please contact the main supervisor associated with the project.  

If you have any queries about the postgraduate application process please contact pg-admissions@aber.ac.uk

Ann Robertson PhD Scholarship 2023 Project Details

Can Colloids Learn: A Path Towards Smart Matter

Dr Adil Mughal (Department of Mathematics) - aqm@aber.ac.uk 

This PhD project aims to develop an innovative method for controlling the self-assembly of patchy colloidal particles, which have potential applications in photonic crystals, targeted drug delivery, electronic and sensor technologies. The ultimate goal is to create smart colloids that can receive chemical or physical inputs and respond with a change in morphology and function, leading to ver-satile and adaptive materials.

Through a combination of numerical simulations and theoretical work, the project will explore a groundbreaking approach to self-assembly using transmutable nanoparticles, driven to crystallize along multiple thermodynamic trajectories. In this system, colloidal particles with complementary patches that enable them to stick together will "learn" to form desired structures through a process analogous to reinforcement learning. The strength of attractive interactions between patches will be adjusted based on the desirability of the arrangement, leading to more controlled self-assembly.

This innovative method has the potential for significant technological impact in various industries, as it addresses current limitations in self-assembly techniques and inverse statistical-mechanical methods. By overcoming these challenges, the project will pave the way for the development of next-generation materials with valuable optical, electrical, and mechanical properties, and enable new applications in areas such as cloaking, chemical sensing, imaging, nonlinear optics, and meta-fluids.

The candidate should have an undergraduate degree in Applied Mathematics, Physics, or a related discipline, with grade II(1) or above. Programming experience is essential.

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Investigating Area Selective Deposition (ASD) as an Enabler for Future Technologies

Dr Anita Brady-Boyd (Department of Physics) - anb116@aber.ac.uk 

As feature sizes continue to decrease in integrated circuit chips, fabricating these devices becomes increasingly difficult. At present Apple’s most current chip boasts an incredibly small 28 nm width for their interconnects, which are the wires connecting the individual transistors. The transistors themselves are ~ 48 nm wide. Area selective deposition (ASD) allows for the nanoscale patterning of materials while limiting the use of traditional photolithographic and etch steps which can be time-consuming, expensive, and wasteful of materials. Although only a new field of research, ASD has been hailed as a driver for next generation technology. This PhD will take a fully interdiscipli-nary approach to answer fundamental questions about ASD with the core objective of the project to progress towards implementing ASD processes into nanoelectronics fabrication. ASD requires the use of small molecules called self-assembled monolayers (SAMs). These molecules create both a physical and chemical barrier to block deposition. The initial stages of the project will involve fundamental study into how these molecules interact and adhere to industry relevant substrates. Different methods of deposition for the SAMs will be investigated. Next, the ability of the SAM to block any subsequent deposited material will be investigated. This will utilise a lot of the world-class equipment already available to the Physics department. The final part of this project will in-volve collaboration with the industrial links of the PI. This stage involves the scale up of our ASD process to high volume manufacturing on 300 mm wafers used in industry.

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Soft matter approaches towards versatile and scalable photonics materials

Dr Chris Finlayson (Department of Physics) - cef2@aber.ac.uk

Polymer nano-spheres (with composite core-shell layers) can be synthesised and arranged into crystal structures, also known as Polymer Opals, to produce intense iridescent colours. In a real advance over other forms of synthetic opals, they are made by standard plastic manufacturing techniques, presenting a promising platform for next generation bulk-scale photonic structures, coatings, and sensors. They are flexible and durable, making them suited for mass production and incorporation into consumer items, and unlike with existing dyes/pigments, they are non-toxic, inexpensive and resistant to fading.

The recently developed bending induced oscillatory shearing (or BIOS) sample preparation meth-ods have had a transformative effect to the ordering and quality of such soft matter photonics. The next challenge is the general application of BIOS in generating a wider range of highly ordered opaline materials with advanced optical functionality. This studentship will offer significant pro-gress on multiple fronts; process development for new functional materials, furthering the re-search underpinning the scale-up to innovative applications, and the underlying science of order-ing in composite soft nanophotonics.

A key challenge is a deeper understanding of the rheological (fluid mechanics) properties of poly-meric viscoelastic media and the exact mechanisms and time evolution of crystallisation under shear flow. A combined experimental and theoretical approach will synergise detailed rheometry with simulation modelling and machine learning (in collaboration with the Maths Department). With applications in mind; the key scale-up of thin-film photonics to roll-to-roll processing, and the associated tolerances and quality control, will be examined using state-of-the-art in-line goniome-try and hyperspectral imaging techniques.

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Characterisation, Calibration and Testing of a Miniature Infrared Spectrometer for Planetary Explo-ration

Dr Dave Langstaff (Department of Physics) - cef2@aber.ac.uk 

Aberystwyth University is building a miniature spectrometer which is proposed to form part of the instrumentation suite for the Rosalind Franklin mars exploration rover due to launch on the re-scheduled European Space Agency ExoMars mission in 2028. The spectrometer, named Enfys after the Welsh word for rainbow, is to operate in the Near Infrared (NIR) and Short Wavelength Infra-red (SWIR) bands and will be used primarily for geological identification but also for studies of the Martian atmosphere. The instrument is required to survive the vibration and shock of launch and landing as well as the extreme cold of the Martian night down to -130C. It will be then required to operate during the Martian day over a temperature range from -50C to +40C.

As part of the development of this instrument, there is an opportunity for a PhD student to work on characterisation and testing of component parts and the completed instrument at low temper-atures; investigation of potential failure modes during thermal cycling, and procedures for pre-launch and in-situ calibration and testing of the completed instrument.

The successful candidate will ideally have experience of instrument design and calibration as well as practical laboratory and software skills. As well as their PhD thesis, they will contribute towards scientific papers written during the course of the project.

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How we See Motion in Depth

Dr David Hunter (Department of Computer Science) - dah56@aber.ac.uk 

This PhD studentship opportunity involves using machine learning techniques to understand how humans process moving objects. Using machine learning techniques on static images has proven a powerful tool in enabling researchers to understand human visual processing. However, extending these techniques to moving images is an on-going challenge. With human subjects this is further complicated by head motion and eye-tracking of moving objects.

Therefore, this PhD studentship opportunity focuses on addressing the complexities associated with analysing motion and object tracking in dynamic environments. As a PhD student in this project, you will be responsible for gathering first-person perspective videos and eye-tracking data from individ-uals performing various tasks. You will use this data to develop AI models that mimic human visual processing. By creating robust models of early-stage visual processing, your work will contribute to advancing our understanding of the brain and its cognitive functions.

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To Develop a Machine Learning for Prediction of Childhood Asthma from Pre-school Information: a Super Learner Ensemble Approach.

Dr Faisal Rezwan (Department of Computer Science) - far8@aber.ac.uk 

Childhood asthma is a major cause of morbidity both in the UK and worldwide and also incurs con-siderable healthcare costs to the nation. A cure for childhood asthma is desperately needed but very little is understood about its early origins. However, at present we cannot predict which children with wheeze will develop asthma or which will outgrow their symptoms. To answer these questions, we need to focus on this early phase of asthma development during first few years of life. Different factors, including demographic, co-existing medical conditions, and environmental exposures, are likely to be important. Given the multiple contributions of epidemiological risk factors to the development of childhood asthma, a new approach is warranted to gain a great insight into disease pathogenesis and better estimation of disease risk. It has been clearly demonstrated that the use of machine learning models in asthma prediction can be beneficial in clinical decision making. Few studies have undertaken research for developing diagnostic or prognostic prediction of school age asthma development using machine learning. However, all of these studies suffer from generalisibility and lacks the approach of explainable AI. Therefore, the aim of this doctoral project is to develop a more robust and efficient prediction models using super learner ensemble approach to predict childhood asthma using larger cohort datasets. For this, we will use multiple cohort data (>14,0000 samples from five cohorts) from the Study Team for Early Life Asthma Research (STELAR) consortium. and further to develop an online prototype tool for predicting childhood asthma.

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Quantum Engineering Thermodynamics

Prof John Gough (Department of Physics) - jug@aber.ac.uk 

The Python package QuTiP will be used to simulate continuous observed quantum systems and study the entropy production. This contributes to the field of quantum thermodynamics which is currently gathering considerable interest: especially to the emerging subfield dealing with stochastic nonequilibrium problems. The novelty here is that we use insights from signal processing, feedback control, as well as the specific expertise of the supervisor in modelling interconnected open quantum systems and networks. There have been prior interpretations of the (Kalman) filter as a “Max-well’s demon” in both the classical and quantum case.

The quantum Kalman was studied in a recent paper but uses the Shannon entropy of the Gaussian Wigner function rather than the correct von Neumann entropy. We seek to remedy this and examine specific case studies. To study this we need to solve the algebraic Riccati equations associated with the filter and here numerical simulations will be necessary. There will be an opportunity to collaborate with international researchers. The project is suitable for a physics/mathematics graduate with experience of Python programming.

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Measurement Resources in Open Quantum Systems

Dr Jukka Kiukas (Department of Mathematics) - jek20@aber.ac.uk 

This project will build a theoretical framework for characterising the dynamical evolution of quantum properties in measurements induced by environmental interaction. This contributes to the re-search area of quantum information theory and correlations, and is motivated by practical applications to quantum technology.

An open quantum system interacts with its environment, which may consist of “noisy” surroundings or monitored ancillary systems. The resulting temporal evolution can be characterised by successive or continual transformations on the state of the system, or, alternatively, on the measurements available for extracting information from the system for further classical processing. This project will focus on the latter — while evolution of quantum properties in states (such as entanglement) have been studied extensively, much less is known about measurement resources.

The main aim is to understand how quantum measurement resources degrade with time in open systems — this is motivated by the development of noisy quantum devices. As measurements are mathematically constructed from the semidefinite cone, one starting point is to study the temporal contraction of the cone and the resulting reduction in specific resources. Preliminary results exist in the case of incompatibility of a given set of measurements, that is, nonexistence of a common refinement which could simulate all of them. This naturally leads to further related topics, including quantum contextuality, steering, and uncertainty relations, among plenty of other possibilities.

The candidate should have an undergraduate degree in Mathematics or Physics, with grade II(1) or above. Interest in the mathematical structure of quantum theory is essential. Master’s degree and/or knowledge on quantum information theory / open systems is desirable.

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Better Multimodal Benchmarks for Theory of Evolutionary Computation

Dr Maxim Buzdalov (Department of Computer Science) - mab168@aber.ac.uk 

Evolutionary computation is a computational intelligence discipline studying randomised algorithms acting as black-box optimisers. Over years it developed sound theoretical grounds with proven results about the performance of algorithms on certain problem classes. Here, properly designed benchmark functions play an important role: they can be used to understand the working principles of evolutionary algorithms by considering in isolation different features that make optimisation problems hard.

One of such features is multimodality, that is, presence of local optima, and the most famous bench-mark function dedicated to multimodality in pseudo-Boolean optimisation is called Jump. However, there is evidence that the Jump function has a number of properties that makes it too easy for some algorithms. This may lead to overly optimistic conclusions about their performance, which do not hold in practice. Recent papers studied few variations of it, but the research field calls for more.

The aim of the project is to develop variations of the Jump function, possibly having multiple parameters that control its shape, that are more realistic in terms of carrying the results over to real-world problems, but still allows rigorous runtime analysis. The project will also require to perform such analysis for a number of evolutionary algorithms, and to augment this analysis with computational experiments that clarify the behaviour for the cases where the theory is not precise enough.

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Why is Reasoning about Function Widely used by Engineers yet so Difficult for Computers? (Computer based Functional Reasoning: Analysis and Automation)

Dr Neal Snooke (Department of Computer Science) - nns@aber.ac.uk 

Engineers use notions of function and purpose as an abstraction to efficiently understand, reason, and explain how and why products and systems work. In contrast, behaviour and structure explain what a product does and is well supported and understood by industry. Despite many decades of academic research there remains little consensus and no explicit standard functional reasoning (FR) approach incorporated into software or tool design used by industry software or design automation tools.

Building on experience from previous work at Aberystwyth on model-based (deterministic, explain-able, deductive) AI tools for a variety of engineering design tasks that rely on functional interpretation, this project will consider the fundamental similarities and differences of the competing functional representations.

The research will determine if an encompassing framework incorporating concepts including multi-view flexible structural hierarchy, context and deployment-mode and separation of interpretative purpose and physical effects is possible. If the different approaches and views of function cannot be reconciled, then a clear schema and understanding of these fundamental differences with respect to a broad range of target tasks such as design, redesign, planning, explanation, diagnosis, failure mode analysis, prognosis, reverse engineering, design verification, etc., will be developed.

Overall the work will support, and guide engineers tasked with approach and tool selection and enhance development of (AI) software and tools that support the product lifecycle and exchange of functional information.

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Anytime Analysis for Dynamic Optimisation Problems

Dr Thomas Jansen (Department of Computer Science) - thj10@aber.ac.uk 

Many optimisation problems are too difficult to be solved efficiently by standard algorithms. Heuristic optimisation methods like evolutionary algorithms are frequently applied in these situations. Theory still puts an emphasis on runtime analysis and is at odds with the way these heuristics are actually applied. This project addresses this gap by concentrating on anytime analysis targeting dynamic problems that change over time.

Building on existing anytime analysis results (sometimes also called fixed budget results) as well as recent results from fixed target analysis, the project performs a systematic study of dynamic optimisation. The starting point are simple static unimodal and multimodal benchmarks and simple different tools to construct dynamic optimisation problems from static ones. Starting from simple heuristics like random sampling and local search the tools and methods are developed to com-pare these baseline methods with more advanced methods, employing populations, crossover, and different approaches to deal with dynamic optimisation problems like hall of fame approaches or diploidy.

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On-site Breed Classification from Low Coverage DNA Sequencing

Dr Wayne Aubrey (Department of Computer Science) - waa2@aber.ac.uk 

This project aims to use Machine Learning (ML) methods and state-of-the-art DNA sequencing to assign cattle by breed origin. There are more than 1000 recognized breeds of cattle globally and ~12 commercially important breeds in the UK. The MinION sequencer by Oxford Nanopore has made the real-time genetic testing of animals on farms affordable. Genetic testing has the potential to increase the value of animals to breeders by providing empirical evidence of breeding lineage, but current methods rely on central genotyping facilities that are time-consuming, with breeders often waiting 4-8 weeks to obtain the result of testing for a given animal. Research by Dr Wayne Aubrey and Dr Matt Hegarty has demonstrated that ML methods can be applied to cattle genomes to clas-sify a set of single nucleotide variants (SNVs) into individual breeds. Identifying SNVs relies on align-ing DNA sequence reads to a reference genome to identify single base differences, which typically results in ~3 million SNVs.

The first step is to establish a database of SNV allele frequencies derived from the 1000 Bulls dataset (121 breeds, over 2000 animals) to train a ML model to identify what combinations of SNVs and their location in the genome contribute the most to breed classification. Reducing the number of SNVs under consideration avoids the computationally slow process of aligning reads to the entire genome. The project also aims to improve the affordability of testing by determining what is the minimum sequence coverage necessary for accurate classification.

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Trust Management in Vehicular Networks using Artificial Intelligence

Dr Yasir Saleem (Department of Computer Science) - yss1@aber.ac.uk 

A vehicular network contains vehicles that could be autonomous, semi-autonomous or with human drivers. Vehicles collect several types of data from the environment that include accident data, emergency data, advertisements, and backup data to name a few. Such information is exchanged with other vehicles using vehicle-to-vehicle (V2V) communication and with the infra-structure, called road-side unit (RSU), using vehicle-to-infrastructure (V2I) communication. In addition, vehicles also exchange information related to their location, speed and heading direction with other vehicles and RSUs. However, in such a scenario, some vehicles may misbehave for their own benefit (e.g., to get more resources) and in this regard, can provide false information or services. Such misbehaving vehicles can either act alone or in a group. Hence, trust management systems have the important role in providing trustable communications, preventing data manipulation by unauthorized vehicles, and can be developed to guarantee the detection of malicious behaviours. Trust management in vehicular networks is challenging because firstly, vehicles meet other vehicles for short duration and secondly, it is less likely that vehicles meet the same vehicles again. It is in-teresting to investigate how Artificial Intelligence (AI) (such as reinforcement learning) can be used for trust management in vehicular networks.

The aim of this project is to investigate trust issues in vehicular networks and develop a trust man-agement system using AI that considers high mobility, high dynamicity, and heterogeneity. The per-formance should be evaluated using a network simulator (OMNeT++ or NS-3 with SUMO) by con-sidering real-world vehicular mobility datasets.

AberDoc

AberDoc Scholarships are part of a prestigious fund for Research Postgraduates.

These awards are tailored to enable students to develop the necessary skills required to meet their career choices and offer a breadth of development opportunities to enhance their research, teaching and transferable skills.

For more information, check out the dedicated page for AberDoc.

AHRC Scholarship

Aberystwyth University is one of a number of institutions in the South, West & Wales Doctoral Training Partnership (DTP) and has successfully secured funding for PhD scholarships in the arts and humanities. Successful students may benefit from potential supervision and training opportunities available at more than one university within the DTP.

Please note that these awards are only available to UK students and are for new PhD students rather than current PhD students.

Please visit the South, West & Wales Doctoral Training Partnership website for further information.

Other Funding

Other funding opportunities are available. For more information check the dedicated Other Funding page.