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You are here Awards > Trinity Research Doctorate PI-Based Awards > PI Based Award Winners 2023-24

Winners of the Trinity Research Doctorate Awards supporting PI-based research projects have been announced!

The Trinity Research Doctorate Awards support staff appointed in or since 2020-21 to recruit doctoral students in the academic year 2023-24.

Dr Deidre Nic Chárthaigh

Assistant Professor, Department of Irish and Celtic Studies

Project Title: Rómánsaíocht Revisited

A large corpus of Early Modern Irish Romantics tales, composed between the 13th and the 17th centuries, survive in Irish-language manuscripts. These include tales belonging to the fianaíocht tradition (such as the well-known Tóraigheacht Dhiarmada agus Ghráinne), translations of Arthurian romances, and modern adaptations of tales from the early medieval period. Despite the linguistic, historical, and literary importance of these tales, they have received little scholarly attention. Many texts have yet to be fully edited, elucidated and translated, and interpretive work on the genre is in its infancy.

Rómánsaíocht revisited’ aims to go some way towards redressing this gap in scholarship by making the material more readily accessible and available, and by bring academics together to consider a more cohesive, systematic approach to the study of Early Modern Irish prose.

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Dr Donatella Camedda

Assistant Professor, School of Education

Project Title: The Inclusion of Students with Intellectual Disabilities in Higher Education (INSIDE)

Professor Camedda is a researcher and lecturer in inclusive education. She coordinates the Art, Science, and Inclusive Applied Practice (ASIAP) programme at the Trinity Centre for people with intellectual disabilities (TCPID). Donatella leads research in intellectual disability and higher education, teacher education for inclusion, and inclusive attitudes. She teaches and coordinates inclusive education modules and supervises postgraduate and doctoral students working in the area of education and inclusion.

People with intellectual disabilities (ID) have historically faced marginalization, particularly in the realm of education. Inclusive education has made strides in Ireland, but students with ID remain under-represented in higher education due to limited opportunities, systemic barriers, and admission criteria. To address these inequalities, independent initiatives have emerged to support university education for students with ID, with recent governmental funding initative PATH 4 aiming to further expand such provisions. The Trinity Centre for People with Intellectual Disabilities (TCPID) at Trinity College Dublin has been a pioneer in inclusive higher education and has established the inclusive National Education Forum (INHEF) to foster networking and peer support among institutions offering similar programs. However, little research has been conducted on the impact of attending university courses for students with ID and their broader effect on the university community.

The Inclusion of Students with Intellectual Disabilities in Higher Education (INSIDE) project aims to fill this research gap by investigating the sustainability of university programmes for people with ID and their impact on students’ lives. The project will be conducted nationwide, involving all Irish higher education institutions providing post-secondary initiatives for students with ID. It consists of two main research objectives: understanding the impact during college attendance and exploring the impact after graduation. The project will employ a mixed-methods approach, incorporating participatory and inclusive research principles, including surveys, interviews, focus groups, and creative methodologies such as photovoice or visual arts. The research tools will be co-designed with input from people with ID who have attended higher education courses.

The project’s outputs will include a PhD thesis, journal papers, conference papers, national workshops, and public engagement activities. The study is expected to have both social and academic impact by providing evidence to sustain the future development of higher education courses for people with ID and promote social justice, equitable access to education, and inclusive society. It will also contribute to the field of inclusive education and the global agenda of inclusive higher education.

Overall, the INSIDE project seeks to generate knowledge that will inform the inclusive development of higher education in Ireland and potentially influence other European countries, while also advancing the academic understanding of inclusive education for individuals with ID.

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Dr Siobhan O Brien

Assistant Professor, Department of Microbiology

Project Title: Fungicide and antimicrobial resistance

Antimicrobial resistance (AMR) is a pressing global issue that is expected to cause upwards of 10 million deaths a year by 2050.  While responsible antibiotic stewardship has been advocated as crucial for controlling AMR, emerging evidence suggests that reducing antibiotic use alone may not be sufficient for curbing or reversing AMR. Anthropogenic activities, such as intensive agricultural practices, are now recognized as important and overlooked predictors of AMR evolution in natural bacterial populations. Agricultural chemicals in particular can be key drivers of AMR in non-target microbes, however we lack causative experiments that bridge the gap between simplified laboratory studies, and correlations based on natural soil communities.

This PhD project will examine how commonly used fungicides drive AMR evolution in the soil-dwelling opportunistic pathogen, Pseudomonas fluorescens using long-term experimental evolution.

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Dr James Ng

Assistant Professor, Statistics in the School of Computer Science and Statistics

Project Title: Synthetic Data Prediction of AAC flare-up risk

There is a growing demand for the integration of artificial intelligence in healthcare systems, driven by the potential to leverage historical patient health data for building powerful predictive models that improve disease diagnosis and understanding. However, the availability of data and privacy concerns present significant challenges in utilizing health information for research and innovation.

To address these barriers, the emerging field of synthetic data offers a promising solution. Synthetic data refers to artificially generated datasets that replicate the characteristics and statistical properties of the original data source while ensuring individual privacy and confidentiality. By preserving key statistical features of the original data, synthetic data serves as a viable alternative for researchers and innovators.

Current synthetic data generation methods in healthcare predominantly rely on large-early scale electronic health records or clinical trial data. However, these methods may not adequately address the challenges posed by small sample sizes, high-dimensional features, and longitudinal rare disease data. In such cases, existing methods may struggle to capture the complex patterns and unique characteristics specific to these healthcare scenarios.

The project aims to advance the state-of-the-art synthetic data generation methodologies for high-dimensional longitudinal rare disease data. This project will focus on the use case of ANCA-Associated Vasculitis (AAV) flare risk modelling and prediction. AAV is a chronic autoimmune disease that requires initial high-dose immunosuppressive drugs (ISD’s) followed by maintenance ISD’s to prevent flare-ups. Accurately predicting the risk of flare could reduce the reliance on ISD’s, resulting in significant benefits for patients, the healthcare sector, and society as a whole. By generating synthetic data for AAV flare risk modelling and prediction, clinicians can utilize predictive models to estimate the risk of flare for individual patients, enabling informed treatment decisions and dosage adjustments.

The proposed research includes the development of an R package and an R shiny App that will empower researchers to generate synthetic data for healthcare applications, particularly in the context of high dimensional, small sample size longitudinal data.

The application of synthetic data in AAV flare risk modelling and prediction can potentially lead to more personalized and optimized treatment plans, reducing the unnecessary use of immunosuppressive drugs and minimizing the associated side effects. Ultimately, this approach can improve patient outcomes, enhance healthcare resource allocation, and contribute to the overall wellbeing of individuals with AAV and the broader society (SDG 6: Good Health).

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Dr Silvia Caldararu

Assistant Professor, Department of Botany

Project Title: PhenoShift: Phenology as a growth and survival strategy in a changing climate

Anthropogenically-driven climate change is the greatest problem of our times. While mitigation – reducing greenhouse gas emissions – is our primary solution, we must be ready to adapt to its effects. Both strategies require being able to quantify how much carbon dioxide (CO2) remains in the atmosphere and how much is taken up by processes on land and in the ocean and how the climate will change, given this amount of CO2, as well as how ecosystems will respond to these changes. The way we make these predictions is by using earth system models, mathematical representations of our understanding of physical, chemical and biological processes. While such models have advanced greatly over the past decades, our knowledge is still incomplete, especially in the biological realm.

Phenology refers to the timing of biological processes, and specifically from a carbon cycle perspective, the timing of plant growth and senescence (e.g., leaf fall). This timing is driven by temperature and precipitation but also by biological adaptions and interactions. Phenology will be affected by climate change, and it will in turn affect the amount of CO2 in the atmosphere and indirectly climate change, as the number of leaves drives terrestrial photosynthesis, the main mechanism for removing CO2 from the atmosphere.

PhenoShift aims to shift the way we model plant phenology. It moves away from the on/off switch that triggers the start and end of the growing season. This will allow us to predict plant growth in regions where seasons are not defined by temperature, which is a large part of the world. The project will not just look at leaf phenology, to represent growth of the whole plant, as a large amount of carbon is stored in roots and stems. To do this, PhenoShift will use a mathematical concept grounded in evolutionary theory – optimality. This theory will be implemented in a large-scale model, of which the PI is one of the main developers. The project will use a combination of data sources to understand the complex processes and scale differences of ecosystem science. These data include satellite measurements of greeness, which are global and span decades, and similar greeness indicators measured automatically at experimental sites.

The new representation of phenology, together with the novel use of multi-scale data will advance our understanding of plant seasonality and improve our predictions of the carbon cycle and impacts of future climate change on land ecosystems.

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Dr Irina Kinchin

Assistant Research Professor, Medical Gerentology (TCIN)

Project Title: Disease-modifying Therapies in Alzheimer’s

This PhD project is designed to prepare Ireland’s healthcare for a significant advancement in Alzheimer’s disease treatment. Alzheimer’s, a type of dementia, affects about 64,000 people in Ireland, a figure that could double by 2045. Current treatments help manage symptoms but cannot halt the disease. However, new treatments called Disease-Modifying therapies (DMT’s) show promise in slowing the disease’s progression, which could let patients live more independently for longer periods.

The PhD student will tackle three main tasks in this project. First, they’ll explore the features and effectiveness of DMT’s in detail. Second, they’ll seek to understand the Irish public’s attitude and preferences towards these new therapies, helping to anticipate how willing people might be to use them. Finally, they’ll evaluate if existing healthcare facilities, like memory clinics, are ready for the possible surge in demand that DMT’s may create and how these facilities might cope with this increase.

Using these three connected studies, the PhD student aims to uncover anything that might hinder or support the successful introduction of DMT’s in Ireland. The research methods used will provide comprehensive insights that can inform future healthcare policies, improve the allocation of resources, and ultimately, enhance the quality of life for Alzheimer’s patients. This research will help ensure that the country is ready for this major development in Alzheimer’s treatment, with benefits for patients, their families, and the healthcare system as a whole.

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Dr Lara McManus

Research Assistant Professor, Clinical Medicine

Project Title: ALS Diagnosis

Amyotrophic lateral sclerosis (ALS) is a nervous system disease characterised by the death of motor neurons, which are the nerve cells that control muscle movements. One of the most important steps in confirming ALS is the detection of abnormal firing patterns in the electrical activity produced by motor units. However, current recording methods using needle electromyography (EMG) are not sensitive enough to detect early signs of ALS.

This PhD project will use non-invasive high density surface EMG (HD-EMG) technology to examine motor unit activity in people with ALS. The aim of this research is to assess whether features of the HD-EMG signals can be used to sensitively detect subtle signs of motor unit degeneration in people with ALS. To do this, dimension reduction techniques and non-linear dynamic measures will be applied to examine the HD-EMG structure and reveal whether changes in the characteristics and firing synchrony of the underlying motor units can be detected. Quantitative measures that can identify subtle alterations in motor unit activity are urgently needed to enable a faster ALS diagnosis and earlier enrolment in clinical trials.

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