You will earn 6 research credits over 8 weeks, conducting faculty-supervised, hands-on, directed study research projects with results that will culminate in the preparation of a research paper. You will complete a minimum of 240 hours on research in and out of the laboratory.
Faculty mentors will work closely with you to direct your continued growth and knowledge development in the chosen research topic discipline.
|DUBI RSLW 392S||International Independent Research in STEM Fields||6|
Recent advances in the field of AI have made it possible to transform a textual description of an object into a graphical realisation of that object in another medium (image, 3D, audio etc.). This project will seek to incorporate state-of-the-art open-source generative models such as Stable Diffusion and Shap-E into a VR/AR environment. In the case of Stable diffusion, the goal will be real-time image-to-image transformation of what the user is seeing, whether in an AR environment or a fully virtual one, based on a given prompt. This prompt is done through voice commands, and can potential be effected by sensory data obtained (stress, heart rate). Potentially could be expanded by having a LLM dictate how the prompt changes. In the case of Shap-E, the goal will be the generation of 3D objects based on verbal user commands. The user will be able to specify the desired object and after a short delay, it will be generated and the user can resize/rotate it and place it in their virtual environment.
Generative AI has captured the public’s imagination. However, tools like ChatGPT do not just augment web searches or aid everyday productivity. They can be used in scientific work of all kinds. However, to-date these tools have typically been used to facilitate already existing studies. The role of Generative AI in scientific discovery is less well understood, but it is possible that Generative AI it may substantially accelerate the process of not only answering new questions but finding new questions to answer.
This project will explore how Generative AI can be used in the process of scientific discovery. This project is very flexible and could focus on any science imaginable – including any number or combination of scientific disciplines. Questions include: How can Generative AI aid scientists in the process of scientific discovery? Is it possible for Generative AI to advance knowledge about existing problems and questions? Is Generative AI capable of finding new questions, the answers to which may help answer similar questions, including those in other scientific disciplines? This project offers an opportunity to work with the supervisor and their PhD students. The student will be an active member of the supervisor’s lab during their stay.
This project is suitable for any major with basic programming experience.
Programming Error Messages (PEMs) – sometimes called Compiler Error Messages – are frequently perceived as cryptic and unhelpful. It is not uncommon for students to ask questions like: What does “illegal start of type actually mean” and how can that help me fix my code?
Generative AI such as ChatGPT has been shown to provide better error messages than traditional compilers, particularly when the broken source code is included in the prompt. However several unanswered questions remain.
This project will use Generative AI to study the types of prompts that are most effective forexplaining PEMs, compare how well Generative AI performs across different languages, and make recommendations for using Generative AI for students seeking help PEMs.
The ideal student will understand the basics of programming and be curious about PEMs and
Generative AI. This project offers an opportunity to contribute to a larger-scale project, work with the supervisor and their PhD students. There is an opportunity for this work to potentially contribute to a scientific publication. The student will be an active member of the supervisor’s lab during their stay.
This project is suitable for all majors.
Computer Science is one of the most influential academic disciplines in terms of impact on society. However, CS has significant issues with equity, diversity and inclusion. Almost every minoritized group is underrepresented in industry and academia. Many of these issues can be attributed to misconceptions about what CS is and what computer scientists do.
These misconceptions are propagated by the media and many other channels.
This project will contribute towards an ongoing project called Computing Crossroads (computingcrossroads.org/). This project features people trained in CS who have gone on to pursue positions in life outside traditional CS, and those who do traditional CS but don’t have traditional CS training. These stories can be inspiring and help to promote better equity, diversity, and inclusion in CS by putting a spotlight on inspiring personal stories of CS folks from all walks of life. These stories can serve to break down misconceptions such as “if you major in CS you are stuck in CS”, “I can’t do CS”, and “it’s too late to do CS now”, as well as stereotypes like “all computer scientists are nerds”.
This provides a great opportunity to do primary qualitative research with real human participants that is aligned with other social sciences. This project will live beyond the internship and provides a great chance for a student to contribute to a larger project with a long lifespan. The student will be an active member of the supervisor’s lab, working with the supervisor and PhD students during their stay.
This project is suitable for any major.
The Blackbox dataset contains programming activity and source code from >250,000 students learning to program in Java using the BlueJ development environment. Blackbox is relatively new and largely underutilized. This project will explore this data working with a PhD student and along with their supervisor, seek to answer questions like the following: What role do compiler error messages play in learning to program? How can they be made better? How does the programming environment affect student learning? Believe it or not, these questions don’t have definitive answers! The answers to questions like these will be of practical use to the overall project and also to the computer science education community in general. The exact nature of the questions to be answered will depend on the progress of the overall project but the intern student can have a part in shaping these questions.
In 2019 an earlier stage of this project resulted in a paper published at the ACM SIGCSE Technical Symposium (sigcse2020.sigcse.org) with the summer student and the UCD PhD student as co-authors.
This project is suitable for students who have a strong interest in databases and programming and hasa good chance of resulting in a publication. Some knowledge of Java and SQL (or willingness to learn ahead of time) is advantageous. This project offers an opportunity to contribute to a much larger-scale project and to see how PhD projects work on a daily basis. The student will be an active member of the supervisor’s lab during their stay.
This project is suitable for any major with basic programming experience.
The integration of artificial intelligence (AI) and machine learning (ML) in medical diagnosis has the potential to enhance diagnostic accuracy and efficiency. This project aims to augment traditional medical diagnostic methods with advanced computational techniques. The project focuses on developing and evaluating a machine learning model for medical diagnosis. The project will encompasses a comprehensive literature review, data preparation, feature extraction and algorithm selection. The rationale for choosing a specific ML algorithm will be justified. The performance of the model will be evaluated using standard metrics and benchmarked against other published systems.
Methods to increase the interpretability and explainability of the models will be explored.
Key ethical considerations around data privacy and security, bias, transparency and accountability will be addressed. The results will inform a discussion that explores the implications, limitations, and potential for future research in the integrating of ML models in clinical decision support systems. The project’s outcomes are anticipated to contribute valuable insights into the practicality, effectiveness, and ethical dimensions of deploying machine learning models in clinical environments, paving the way for future advancements in this intersection of computer science and healthcare. The goal is to work towards providing medical professionals with robust and interpretable tools to support their clinical decision making.
This abstract provides an outline which can be adapted and customised based on the specific interests of the student, and whether the project is being completed by a single student or a group of students.
It is well-known that surveys often result in inaccurate preference elicitation. This could be due to biases of the surveyer, or limitations of the survey techniques (https://www.qualtrics.com/uk/experience-management/research/survey-bias/). This project aims to check if using conversational agents (aka chatbots) can be used to derive the true preferences of a sample, either in conjunction with other mechanisms or as standalone tools. The project will involve collecting multiple ethical dilemmas, and using them as decision-making contexts. It should then identify whether an interlocuter's responses to both, surveys as well as chatbot-enabled conversations can be used to elicit their true preferences. What kinds of ethical values / dilemmas can be represented, and how to present these to human users, are questions that the project will seek to answer. The student will be required to construct a chatbot, that will present different dilemmas to users, and graph their ethical preferences. The project will use open-source libraries for conversations, such as Llama AI model. This is a coding-focussed project, and the student will ideally be a self-starter, comfortable with handling multiple git repositories, as well as thinking about how to store and present results, in the form of appropriate graphs.
Nudge theory has arisen from the Kahneman and Tversky's seminal work on Behavioural Economics. It shows that people can make repeated, predictably irrational decisions, due to cognitive biases that exist among all humans. Nudges are behavioural interventions that arise primarily from human decision-making frailties (e.g., loss-aversion, inertia, conformity). However, not all humans are affected by the same biases, i.e., a nudge that works on one person may not work on another. BigData, when combined with Machine Learning, promises to create a potentially infinitely flexible nudging machine. A software could learn the specific set of biases that affect a particular person, and then adapt its nudges in a highly tailored manner. This is a HyperNudge. Conversational AI (or chatbots, as they are commonly known) have become an acceptable part of online interaction. ChatGPT is a versatile conversational AI agent that can be used to create decision-making situations. By presenting decision-making situations in a stylized manner, ChatGPT can be used to detect biases among users. This project will focus on creating customized versions of ChatGPT for revealing different biases among different users. This project will require students to be self-starters, able to read papers, come up with hypotheses, and design experiments to provide evidence for/against the hypotheses.
An ability to program in python, and comfort with using multiple open-source Git repositories would be highly recommended.
This research proposal will introduce a novel multitask deep learning model designed for the simultaneous estimation of valence and arousal values, along with engagement classification. Motivated by the importance of understanding human emotional states and engagement levels in various applications, we propose a unified framework that leverages shared representations to enhance the model’s capacity for capturing nuanced emotional content.
The model architecture incorporates shared layers responsible for extracting common features from diverse input modalities, such as affect features, and behavioral features. Additionally, task-specific branches dedicated to valence estimation, arousal estimation, and engagement classification are integrated, allowing the model to simultaneously address multiple facets of affective computing.
For the training process, we advocate the utilization of benchmark datasets such as AFEW-VA, AffectNet, and DAiSEE. This choice ensures the model is exposed to diverse and comprehensive emotional data, fostering its ability to generalize effectively. The training process involves the optimization of task-specific parameters through individualized loss functions for valence estimation, arousal estimation, and engagement classification.
The concurrent learning of these tasks aims to capture shared features, contributing to a more holistic understanding of emotional states and engagement dynamics.
Our approach extends beyond conventional emotion recognition by incorporating engagement classification as a complementary task. This addition caters to scenarios where student engagement is crucial, enhancing the model’s applicability in educational settings, such as in human-computer interaction and multimedia content analysis for learning environments.
Acquisition of satellite remote sensing image (RSI) data for Earth observation can realize applications in various fields, such as disaster recovery, climate change, urban development, and environmental monitoring. Machine learning technology provides an efficient and accurate way for the detection of RSI images, and is commonly used in the area.
In this project, we aim to discover an intelligent system that uses a machine learning-based model and various satellite image processing techniques such as multi-spectral method and spectral indices method. We will complete this project as follows: Firstly, an image processing method will be deployed to regenerate different datasets using current satellite image datasets with multiple channels. Index generation process will also be applied to generate the datasets. Secondly, different machine learning models will be taken to build up the image classification system. We are going to seek a generation of datasets as well as an introduction of a machine learning model that can result in the best performance on image classification. Our developed framework will greatly assist the research in geographic image detection and will be of great use in the aforementioned field.
In the realm of advertising, the strategic placement of billboards plays a crucial role in maximizing their visibility and impact . Sustainable development and urban planning involves the automatic categorization of land cover to identify optimal locations for billboard placement. Therefore, continuous monitoring and identifying suitable urban areas for billboards placement paramount importance for industries such as marketing, urban planning, and advertising, facilitating enhanced targeting, analysis, and decision-making .
The primary objective of this project is to develop a comprehensive understanding of urban dynamics by analysing the interplay between various factors using state-of-the-art satellite technology. This involves identifying high-visibility populated locations and analysing traffic patterns for billboard placement in urban areas. We integrate high-resolution satellite imagery  and nighttime satellite data  with machine learning techniques to enhance the analysis of traffic patterns, population density, and detailed mapping of urban landscapes.
Our methodology begins with acquiring high-resolution satellite imagery, integrating it with daytime and nighttime data to capture urban areas. We will develop machine learning techniques and statistical analysis to gain insights into the most effective areas for billboard placement, contributing to more informed decision-making in the realms of marketing, urban planning, and advertising.
Solar energy, a crucial solution to the long-term energy crisis, will stand out as a sustainable power source. However, its integration into the conventional power grid will encounter a significant challenge – the inherent variability and intermittency of solar power production,especially at high PV penetration levels, impacting power system control, grid integration and planning . While current efforts analyze ground-based images and meteorological observations for solar data acquisition [2-3], there is a critical need for an accessible and affordable solution suitable for residential settings. We aim to address this need by creating a compact sensor system capable of collecting diverse environmental and system parameters, including sky images, solar irradiance, temperature, humidity, and wind speed.
The streamlined design will integrate multiple cameras for image capture, complemented by a simplified solar tracking mechanism to enhance solar energy data collection. A dedicated motherboard, housing our proprietary algorithms, will be enclosed alongside essential components. The infrastructure will include multiple cameras and a solar tracking system with a wireless sensor network, where dedicated monitoring stations will acquire earth observation parameters. This data will be transmitted to a cloud server using a wireless network and a low-power IoT protocol. By employing advanced deep neural net and machine vision techniques, we will aim to accurately estimate and forecast solar energy output from photovoltaic (PV) panels and enhance images from a low-cost camera module.
This innovation will overcome the limitations of existing systems, making it suitable for widespread deployment in residential homes.
With an increasing mainstream focus on the security and privacy of user data, built-in encryption is becoming commonplace in consumer-level computing devices. Under these circumstances, a significant challenge is presented to the lawful digital forensic investigations where data from encrypted devices needs to be analysed. This project will explore the use of electromagnetic side-channel analysis (EM-SCA) for the purpose of assisting digital forensic investigations on encrypted data devices. EM side-channel analysis is a technique where unintentional electromagnetic emissions are used for externally eavesdropping on the operations and data handling of computing devices.
The non-intrusive nature of EM side-channel approaches makes it a viable option to assist digital forensic investigations as these attacks require, and must result in, no modification to the target device. Working with this ongoing project in my research group will expose the research intern to the full research lifecycle from research design, development, experimentation with devices and datasets, international collaboration, and dissemination/discussion of results at UCD Forensics and Security Research Group meetings. In addition, the intern will gain experience with software defined radios, digital forensics and cybersecurity principles, internet-of-things devices, and deep learning/data science.
Passwords have been and still remain the most common method of authentication in computer systems. From accessing your smartphone, to setting up your online banking account or social identification, there is a plethora of passwords that users are required to set and remember in hundreds of websites. Complex passwords make the job of law enforcement engaged in a digital investigation more difficult, especially since time is often of the essence.
This project aims to provide insights into the password selection process of users and the impact of contextual information in it. Additionally, the ways that this contextual information can be leveraged in order to assist with the password retrieval process will be explored. The approach to evaluating the impact of context on the password selection process, focusing first on the community. To this end, intelligent, community-targeted dictionaries will be assembled. For example, when targeting a community of superhero enthusiasts, we assume and want to prove that a larger proportion of passwords would be contextually relevant to the community than not.
The work of the intern would involve contributing to the benchmarking process through a framework for evaluating different password cracking methods, as well as evaluating existing and newly created “smart” dictionaries against existing password datasets. The tools and dictionaries will be evaluated for different metrics and for varying degrees of contextualization, with the aim of establishing the impact of context in passwords. Additionally, the intern can contribute in the dictionary creating process and be able to test their own dictionaries with this framework.
The task of multimedia geolocation is becoming an increasingly essential component to effectively combat human trafficking and other illegal acts. While text-based metadata can easily provide geolocation information with access to the original media, this metadata is stripped when shared via social media and common chat application. Geolocating, geotagging, or finding geographical clues in the multimedia content itself is a complex tax.
While there are numerous manual/crowdsourcing approaches to this, recent research has shown that computer vision is one viable avenue for research.
The work of the intern would involve working in a team in the creation of novel datasets for multimedia geolocation and developing computer vision-based techniques for indoor multimedia geolocation. The aim is to develop powerful methods for image geolocation that enable more efficient investigations in the field of human trafficking. Colour values serve here as a key component to describe specific characteristics of an image and colour-based descriptors will be used for Content-Based Image Retrieval. The performance of the developed methods will be evaluated using the Hotels-50K dataset as a foundation. Working with this ongoing project in my research group will expose the research intern to the full research lifecycle from research design, development, experimentation with devices and datasets, international collaboration, and dissemination/discussion of results at UCD Forensics and Security Research Group meetings.
A large language model (LLM) is a type of artificial neural network designed for understanding and generate human-like text. It is a subset of models within the broader domain of natural language processing (NLP), which focuses on enabling machines to interpret, generate, and respond to human language. This project aims to develop a novel approach for digital forensic investigation using language models, such as ChatGPT/BERT/LLaMa. The proposed method will evaluate the potential for LLMs to enable digital investigators to analyze and interpret digital evidence from a variety of digital data sources and devices. Specifically the project will look at the prospect of natural language digital forensic query being taken an input, a step by step process being defined to answer the query, automating the evidence discovery through generated scripting, interpreting the results and presenting this interpretation back using natural language.
The work of the intern would involve working in a team in the creation of end-to-end tests to access the viablity of LLM-aided digital forensic investigation. Working with this ongoing project in my research group will expose the research intern to the full research lifecycle from research design, development, experimentation with devices and datasets, and dissemination/discussion of results at UCD Forensics and Security Research Group meetings.
Campylobacter jejuni is the most prevalent cause of foodborne bacterial gastroenteritis world-wide andthe world health organisation has classified it as a pathogen of concern due to the increasing numbers of strains with resistance to fluoroquinolone antibiotics. We recently published a paper which highlighted that fluoroquinolone resistant strains of C. jejuni appear to have different phenotypes to those of the parent strain including increased virulence and decreased motility. This project will focus on the comparison of antibiotic resistant and sensitive strains using a variety of assays including motility assays, invasion assays as well as molecular characterisation of the genetic mutations which cause this resistance. As part of a collaboration with UCDavis strains isolated from a variety of different sources will be used to select for fluoroquinolone resistant isolates which will be assessed using these assays.
Methods used will be similar to those outlined in our recent paper on acquisition of fluoroquinoloneresistance and its relationship with biofilm formation and pathogenicity.
Students interested in taking this project should be highly motivated and interested in the field of infection biology, antimicrobial resistance and understanding how bacterial pathogens cause disease.
Whelan MVX, Ardill L, Koide K, Nakajima C, Suzuki Y, Simpson JC, Ó Cróinín T. Acquisition of fluoroquinolone resistance leads to increased biofilm formation and pathogenicity in Campylobacter jejuni. Sci Rep. 2019 Dec 3;9(1):18216.
We have shown that suboptimal levels of the essential micronutrient selenium (Se) are associated with an increased risk of developing colorectal cancer (CRC) (Hughes et al. 2015 PubMed PMID: 25042282). This finding was more apparent in association with circulating concentrations of the Se-rich antioxidant selenoprotein P (SELENOP) protein. We further showed a lower expression of SELENOP in colorectal adenomas and tumors (Hughes et al. 2018, PMID: 30469315) and how genetic variants in the Se metabolism pathway were associated with CRC risk and in interaction with Se status biomarkers (Fedirko et al. 2019, PMID: 31027226). Other investigators have also recently showed that SELENOP expression increased as normal colon stem cells transformed into adenomas that progressed into carcinomas (Pilat et al 2023, PMID: 37166989). This group further observed that interactions between the LRP5/6 proteins and SELENOP modulated WNT activity, which is an established signaling pathway in colorectal carcinogenesis. Our previous studies of Se pathway genetic variation observed that 4 SNPs in the LRP2 gene were associated with CRC risk, but we haven’t examined variation of the LRP5 and LPR6 genes before. In this project, in a patient cohort from Ireland with colorectal adenomas, cancers, and controls without disease (as described in Mukhtar et al. 2022. PMID: 35807897), we will assess common genetic variation in the LPR5 and LPR6 genes to test:
Genotyping will be done by TaqMan allelic discrimination assays. This project will further provide evidence of the importance of the LRP/WNT signaling pathway in colorectal carcinogenesis, especially in populations like in Ireland and Europe, of suboptimal Se status and hence SELENOP concentrations.
My lab studies inherited forms of motor neuron disease, particularly hereditary spastic paraplegia (HSP). Individuals with HSP develop weakness in their legs leading to difficulties walking which is caused by degeneration of the very longest motor neurons. Extensive work in recent years has successfully identified many of the genetic causes underpinning HSP, but there are currently no treatments to prevent, cure or even to slow the course of these diseases. To address this, my lab use cutting-edge genetic engineering to generate novel animal models of HSP in the fruit fly, in which we can study the molecular events underpinning this disorder. Recently, researchers in my lab have found that HSP-causing genes play a role in the organisation of the endoplasmic reticulum (ER) network within motor neurons. The aim of your project will be to study how this impaired ER network contributes to neurodegeneration in motor neurons. You will learn various techniques associated with molecular genetics, confocal microscopic image analysis and the assay of behavioural readouts.
Lab website: fniamhy.wixsite.com/osullivanlab
Antimicrobial resistance is a massive growing problem in the fight against bacterial infections. The number of antibiotics that are effective at treating many bacterial infections is shrinking. In particular the ESKAPE pathogens are a group of highly virulent pathogens that can escape the majority of antibiotics. Vaccines represent one of the best ways to prevent bacterial infections and have also been shown to reduce antimicrobial resistance , .
We focus on discovering candidates in order to prevent these difficult and challenging infections. We use a proteomic approach to identify highly effective vaccine antigens which prevent infections in mouse models. We have number of vaccine projects ongoing in our laboratory against antibiotic resistant infections such as respiratory infections that impact the lives of people with cystic fibrosis  ; the tropical infection, melioidosis ,  ; O157 E. coli and three of the ESKAPE pathogens, namely Klebsiella pneumoniae, A. baumannii and P. aeruginosa , . We mapped the global prevalence of multidrug-resistant A. baumannii and showed that carbapenem-resistant A. baumannii is widespread throughout Asia and the Americas  . We have tested these vaccine antigens in mice and are currently examining the protective immunological responses, including antibody responses and cytokine responses in serum or immune cells. This project will focus on the vaccines for ESKAPE pathogens. It will involve using ELISA to determine the levels of antigen specific IgGs in immunised mice. In addition the host response will be further examined by exposing immune cells to antigen and evaluating the profile of cytokines produced using flow cytometry, ELISpot and/ or ELISA. Understanding how the antigens protect against infection is an important stage in progressing the vaccines towards human trials.
Intestinal fibroblasts have emerged as modulators of immune responses. This is of interest in inflammatory bowel diseases, a group of debilitating conditions including ulcerative colitis (UC) and Crohn’s disease (CD). Current therapeutic management of these diseases includes broad anti-inflammatory drugs, immunosuppressants and modern therapies blocking specific cytokines such as tumour necrosis factor (TNF). However, a high percentage of patients do not respond to anti-TNF therapy, whereas other patients may become resistant to this therapy overtime. In this context, inflammatory fibroblasts have been found in tissue samples from IBD patients that display resistance to multiple therapies, including anti-TNF. However, the mediators and mechanisms that define fibroblast-mediated inflammation remain unknown.
Our lab has identified a cytokine termed TWEAK as a modulator of this inflammatory phenotype in human colonic fibroblasts. Our data indicates that this mediator converts fibroblasts to a phenotype characterised by expression of cytokines, chemokines and immune receptors. More recently, we have found that pre-exposure to TNF increases the expression of the receptor for TWEAK, termed fibroblast-inducible 14 (Fn14), in the surface of intestinal fibroblasts. Based on this evidence, we hypothesize that previous exposure to TNF could prime fibroblasts to a stronger response to secondary stimulation with TWEAK and enhance inflammation in these cells. To test this hypothesis, we will perform protein analysis by flow cytometry, western blot and enzyme-linked immunosorbent assay (ELISA) to investigate the effects of TNF and TWEAK in isolation or after sequential TNF-TWEAK treatment on the expression of chemokines, adhesion molecules and activation of inflammatory signalling pathways.
Background: Current treatments for prostate cancer mainly target the Androgen Receptor (AR), however despite initial response these treatments fail. Serum response factor (SRF) was previously identified as an important transcription factor in in vitro models of castrate-resistant prostate cancer (CRPC) and a cross-talk between AR and SRF was demonstrated. To further understand this cross-talk, we used mass spectrometry to identify common interactors between these two proteins. The aim of this project is to manipulate the key common interactors to assess cellular response and to study their signalling pathway in prostate cancer cell lines.
Techniques: Cell culture, MTT assays (cell viability), colony forming assay (cell proliferation), Incucyte (cell proliferation for combination treatments), treatment with small molecule inhibitors, western blotting (protein expression) and luciferase assays (protein activity).
There has been no new therapeutics for stroke brought to the clinic over the last 30 years. Recent promising new research has demonstrated that inhibition of the prolyl hydroxylase enzyme (PHD) and activation of hypoxia inducible factors (HIF) protects brain neurons in ischemia (Puzio et al., 2022, 2023, Moreton et al., 2022). Recently our Laboratory has produced exciting data to indicate that this enzyme has significant protective effects on synaptic transmission during hypoxia and oxygen glucose deprivation (OGD; Puzio et al., 2023). Therefore the central hypothesis of this project is that treatment with PHD inhibitors may alter the sensitivity of neurons to ischemia in the brain.
The student's aims will be to:
Urea is the main nitrogenous waste product in mammals. UT-B urea transporters facilitate the movement of urea across cell membranes and are distributed in a wide variety of tissues - such as kidney, bladder, prostate, colon and brain. They are known to play important roles in the mammalian urine concentrating mechanism, symbiotic relationships with colonic bacteria, and the removal of the toxic urea from the body. Recent studies have shown alterations in UT-B transporters are linked to a wide range of clinical conditions, such as bladder cancer, colon cancer and potentially Alzheimer’s Disease. Utilizing cell culture, RT-PCR, western blotting and immunolocalization techniques, this project will investigate the cellular pathways involved in the physiological regulation of UT-B urea transporters.
More specifically, it will utilise human cell lines from various tissues to investigate the effects of osmolality, external urea, protein kinases and ubiquitination on these important transport proteins.
The development of new therapeutics requires that they are first evaluated using a series of in vitro cell-based assays to assess their efficacy and toxicity. Imaging approaches to measure toxicity, including metabolic activity and ultimately cell death, are powerful, as they provide visual and quantitative data with respect to how cells respond to a drug.
Traditionally these cell-based assays have been carried out using cells growing as monolayers, as they are relatively simple to grow and their analysis is straightforward. However, it is increasingly being realised that more complex in vitro models that better represent the forms that cells take in vivo are needed. This project will focus on the development of a cell-based toxicity assay in which human cancer cell lines are grown as 3D assemblies, termed spheroids. Various fluorescent tools will be evaluated, which have the potential to provide a readout for the state of these spheroids as they grow.
Automated fluorescence microscopy will be used to collect data from the spheroids, and an assay will be developed in which their response to a drug can be quantitatively measured. Results from this assay carried out in spheroids will be compared to results obtained from monolayer-grown cells, allowing us to conclude whether a spheroid model is suitable for such toxicity studies.
Inter-individual variability in animal behaviour within wild populations is an important component influencing their resilience to external perturbations such as human disturbance, climate and habitat change. When such differences are consistent through time, we typically refer to them as personality traits. This project aims to test the hypothesis that behavioural traits recorded at capture (e.g. reaction to capture, vocalization, and behaviour at release) in fallow deer fawns of Phoenix Park are highly repeatable over multiple captures and are a good proxy for their personality. In short, we expect bold fawns to display bold behaviours across multiple captures, and this may predict their life history. The student will join the Phoenix Park capture team during the fawning season of June 2024 and will collect behavioural data on more than 100 fawns. The Phoenix Park is the largest urban park in Europe, it can be easily reached by bus from UCD, and would give the student the opportunity to enjoy both UCD campus and the field site located in the heart of Dublin. The student will learn how to capture, handle and release deer fawns in June, leaving July for data analysis. This is a unique opportunity for students to gain experience in the field of wildlife research and join an experienced team of 20 students and academic staff with long-term experience in animal behaviour.
Systematic camera trap surveys are important for gathering information on terrestrial wildlife. Such surveys reveal distributions, abundances, and behaviours that can inform conservation and wildlife management by providing evidence of animal presence at known locations and times. However, in Ireland, international-standard camera trap surveys were not undertaken to inform the management of large terrestrial wildlife until 2022. The Laboratory of Wildlife Ecology and Behaviour has now established two major projects monitoring wildlife using non-invasive camera traps. The first one - Snapshot Europe - is a coordinated and standardized camera trap effort to collect data on mammals across Europe. The initiative is supported by Euromammals and the Max Planck Institute of Animal Behaviour, in partnership with Snapshot USA. The second one – bioDEERversity – is a DAFM-funded monitoring program set up in the Wicklow and Dublin mountains. Both projects have been gathering data on wildlife and biodiversity in general (soil ecology, plant diversity, and vertebrate relative abundance and diversity) in one of the deer hotspots of Ireland, where Sika deer have been shown to occur at unsustainable high densities.
Camera traps have been set up to capture data in areas with no deer (fenced exclusion zones) as well as in those area spread across a gradient of sika deer relative density. The students involved in the project will analyse camera trap data, identify animal species, and digitize the data, and will have the opportunity to tackle a research question that will be defined with the help of the supervisor (e.g.: what is the effect of deer presence on the occurrence and diversity of the other mammal species? What is the temporal and spatial overlap among the different species captured by camera trapping?).
Discover the microbial world of fermented beverages! This project will introduce the student to cutting-edge sequencing to find out microbial communities that constitute the microbiome of water kefir. The students will focus on the identification of potential pathogens can improve health, and others that affect how kefir is stored and lasts. The study will help make food safer and propose new ideas on safety management of food. The project also aims to understand the symbiotic relationships within the kefir microbiome that may contribute to its health benefits and shelf life.
The student will grow almost single cultures of kefir microbes using a special chip, then identify them using the latest genome sequencing methods. Additionally, the project will offer hands-on experience in microbial culture techniques and in-depth data interpretation to distinguish between beneficial and harmful microbial strains. The project includes introduction to genomic analysis using bioinformatics tools and the students will be part of a team that’s discovering novel bacterial species with beneficial features for human health using a database that we are developing.
Introduction and aim. How safe is our food? In this project, the students will use metagenomics, to identify pathogens in food and food-producing environments. The focus will be on Listeria and E. coli in real-world settings. The research will help improve how we keep food safe. Students will also assess the efficiency of metagenomic approaches in comparison to traditional microbiological methods for a more comprehensive understanding of food safety.
Techniques. The student will collect and examine DNA extracted and purified from various foods and food-producing facilities and then used to identify bacterial species. The project include introduction to genomic analysis using bioinformatics tools (including machine learning approaches) and comparing databases with the aim of understanding how these microorganisms live and spread. The analytical phase will integrate the use of high-throughput DNA sequencing and bioinformatic tools to pinpoint the presence of pathogens and their genetic traits.
Introduction and aim. Dive into the mystery of microbes that are hard to find and grow in the lab, some of which are important for gut health. During the course of the laboratory experience, the student will use selective sequencing methodologies to try and piece together their complete genetic information from complex microbial communities. This exploration will shed light on the roles these microorganisms play within their ecosystems, particularly their influence on human health.
Techniques. The student will use advanced computer methods to analyse DNA data, focusing on specific microbes interesting for human health. The project will challenge students to develop precise bioinformatic techniques to capture and characterise the elusive genetic material of these microbes. The project sets new standards for detecting and understanding microbes that are less-known.
Introduction and aim: The omnipresent nature of bacteriophages (viruses that infect bacteria) makes them a resource of undiscover potential, particularly in the fight against bacterial pathogens. This project intends to isolate and characterise novel bacteriophages from environmental samples collected across diverse regions of the campus. The aim is to isolate bacteriophages with anti-pathogenic capabilities. By understanding the diversity of bacteriophages, students can contribute to the development of alternative treatments for antibiotic-resistant bacterial infections.
Techniques. Collect samples around campus will be tested for the presence of bacteriophages that will be tested for their activity against pathogens. Sequencing approaches will used for the detailed genomic characterisation. The project will also involve collaborative efforts to correlate phage characteristics with their potential applications in biotechnology.
The project includes data analysis and introduction to bioinformatics taking part in a team involved in the discovery of novel microorganisms that might have potential benefit for health and technology.
Introduction and Aim. What’s hiding in fermented foods? In this project, the student will investigate microorganisms from different sources, including milk kefir and cheese. By studying the microbiology through classical culture methods and novel culture-independent approaches, the student will identify how these communities change and interact, which is crucial for food quality and safety. The project aims not only to catalogue these communities but also to delve into the metabolic interactions that drive their contributions to food fermentation.
Techniques. The student will use the latest sequencing tools to get DNA from real food samples. Then, with bioinformatics tools, the student will analyse the data to predict the roles of the microbes and discover new features and describe how they live together. Beyond sequencing, students will engage in experimental designs to culture and investigate these microbial communities, enhancing their laboratory and analytical skills.
We wish to offer this unique summer research project that will provide a talented student with the opportunity to acquire important laboratory skills in a project at the interface of organic chemistry, synthetic medicinal chemistry and chemical engineering. The synthesis and spectroscopic characterisation of a small collection of drug-like building blocks will be targeted with a specific emphasis on making new biological glues that may increase the affinity of protein targets towards 'sticking together'. This will involve using bespoke continuous flow reactors available in the newly established UCD Flow Chemistry lab. Light in the visible range of the spectrum will be used to functionalise the molecules studied which will contribute to greener chemical reactions. Continuous flow chemistry is a novel and exciting addition to the chemist’s toolbox that allows to perform chemistry in a safer, more effective and highly reproducible manner yielding readily scaled and automated processes that are highly desirable in academia and industry alike. This approach will avoid the isolation of unstable intermediates and provides a powerful route into important bioactive structures. Using light as a traceless reagent in combination with in-line purification techniques will result in a green and sustainable technology that highlights the power of modern chemical synthesis in delivering important drug-like structures. The ability to automate this process is highly advantageous as it circumvents tedious downstream processing to yield clean products.
The successful student will be embedded in our international research group (currently 12 PhDs, 2 postdocs) and gain new skills in chemical synthesis, purification, spectroscopic characterisation as well as the use of modern flow reactor technology. For an example of a past project that was subsequently published, please see: https://doi.org/10.1016/j.tetlet.2021.153522
As the body’s information repository, DNA provides the essential role of programming all biological function. This information is translated through numerous molecular interactions.
Proteins and small molecules bind to DNA through a variety of modes that include groove binding, electrostatic interactions and intercalation. The nature of these interactions is influenced by structure of the molecules and the secondary structure of DNA, which in addition to the common B-DNA form can also adopt other non-canonical forms. Two important structures are guanine rich quadruplex and cytosine rich imotif DNA. Quadruplex DNA comprise stacked tetrads of four guanine bases while imotif DNA is an arrangement of alternating interdigitated C:C+ base pairs formed between hemi-protonated cytosines.
The presence of quadruplex and i-motif DNA structures in the cell has been confirmed by fluorescence microscopy. The binding interactions of small molecules to these structures is of interest due to their potential as therapeutic targets. The quadruplex structure is also of interest as it is formed from human telomere DNA. In this project we aim to investigate the binding of metal complexes to distinguish the different structures, which is essential to resolving their biological roles. The project allows for spectroscopic measurements and DNA binding studies which will provide experience across a range of diverse techniques including circular dichroism, UV-visible and emission spectroscopy. This will build on our extensive experience in this field [Chem. Sci., 2017,8, 4705-4723, Chem. Commun., 2020,56, 9703-9706, Chem.Eur J. 2020, DOI: 10.1002/chem.202002165, J. Am. Chem. Soc. 2023, 145, 39, 21344–21360, doi.org/10.1021/jacs.3c06099]
We are interested in preparing gold nanoparticle systems to complement radiotherapy treatment for resistant cancers [ACS Appl. Nano Mater. 2020, 3, 3157]. This relies on the fact that X-ray irradiation of AuNPs releases electrons that form reactive oxygen species that can kill cells [Sicard-Roselli, C. et al. Small 2014, 10, 3338–46] see (1) below. Nanoparticles have a high surface area which allows them to carry molecules that can bind to their surface. We have developed methods to prepare composite particles using polystyrene nanoparticles with high loadings of Au NPs and demonstrated uptake in cells [Chem. Commun., 2016,52, 14388-14391] (see 2 below) and to enhance radiotherapy towards radiation resistant breast cancer cells [ACS Applied Nano Materials 2020, 3, 4, 3157-3162]. In this project the aim is to replace polystyrene spheres with tiny glass beads in the form of biocompatible silica (SiO2) nanoparticles to prepare gold nanoparticle composites. Silica particles also have the advantage that we can load their transparent structure with luminescent molecules to act like tiny light bulbs and allow them to be tracked in cells. Furthermore, over time silica are degraded by enzymes in the body. The low toxicity, high surface area and ease of functionalisation of silica nanoparticles makes them attractive systems for cellular imaging, diagnostics and therapeutics. The project will involve metal nanoparticle synthesis, spectroscopic characterisation, composite preparation and characterisation as well as assaying the stability of the composites formed to biological media. It will also be possible to consider the use of silica nanoparticles to transport photothermal therapeutic agents to cells.
This project will investigate whether a controlling factor uncovered in our laboratories for an intramolecular carbon-carbon bond forming reaction (intramolecular Heck reaction) can be translated to achieve selectivity for a new type of substrate. In general, questions concerning selectivity in reactions are an important topic and it is our ability to direct reactions that enables the controlled and efficient construction of complex carbon-based compounds.
Several years ago we found that we could use a sulfonyl group to help template the construction of a quaternary all-carbon centre in an intramolecular Heck reaction.1 For instance, when sulfonamide 1 was treated with palladium only compound 2 was formed and none of the regioisomeric potential Heck products, 3a/3b were detected. This surprising selectivity enabling carbon-carbon bond formation to take place at the more substituted and hindered end of a trisubstituted alkene proved to be general for dihydropyrroles and was applied for the preparation of several Sceletium alkaloids.2
In contrast, when the intramolecular Heck reaction was conducted with piperidiene 4 an equal mixture of products, 5 and 6, were observed. This stems from a lack of selectivity for the carbon-carbon bond formation phase of the intramolecular Heck reaction . Based on the high degree of selectivity observed for 1 we plan to build compounds 7 and 8 and to see if the presence of the methyl substituent changes the unselective Heck reaction into a process that can be tuned to preferentially provide either isomer 9 or 10. The ability to selectively form these types of compound would be very useful and several natural products and compounds of biological interest possess this type of skeleton.
Ideal for a student with some experience in basic organic chemistry and will involve the day-to-day safe execution of reactions in a chemistry research laboratory; the purification of the products by chromatography and recrystallisation, and the use of analytical techniques (particularly nuclear magnetic resonance spectroscopy) to determine the outcomes of the reactions.
Glycans, carbohydrate patterns on the surface of proteins, play important roles in the function of life. Boronic acid-based chromophores (BA) form reversible complexes with carbohydrates, leading to fluorescence switching (selective sensing). In this project you will synthesise new functionalisable boronic acid-derived probes and screen their glycan/carbohydrate selectivity by NMR, UV and fluorescence spectroscopy, towards the aim of imaging cellular sub-structures, and/or detecting bacterial proteins. Mounting sensor BAs on supramolecular polypeptide scaffolds will help control glycan-specificity further.
This project is part of a UCD STEM Challenge project collaboration with the Palma and Simpson labs.
Many pathogens, such as bacteria P. aeruginosa or fungus C. albicans, produce characteristic carbohydrate-binding proteins. These pathogens are designated as ‘Critical Priority’ by the WHO, requiring new targeting strategies. We have recently shown that luminescent glycoconjugate-lanthanide complexes can detect this class of proteins by characteristic changes in luminescence. You will expand the family of sensors, learning skills in carbohydrate and coordination chemistry. This project aims to find more efficient sensors. Protein sensing and/or antimicrobial behaviour of these materials will be probed and characterized either in the School of Chemistry, or with collaborators. (Further reading: K Wojtczak, E Zahorska, IJ Murphy, F Koppel, G Cooke, A Titz, JP Byrne*, “Switch-on luminescent sensing of unlabelled bacterial lectin by terbium(III) glycoconjugate systems”, Chem. Commun., 2023, 59, 8384; 10.1039/D3CC02300A)
The Wittig olefination reaction has just got a very significant makeover. First, the annoying phosphine oxide by-product can now be used as the starting material. It can be converted directly to quaternary phosphonium salt, QPS, via new fast and high-yielding “Umpolung quaternization”. We have eliminated the waste problem and olefinations can be run using phosphine oxide and avoiding phosphines at the interim stages.
Second, we have developed novel ion-pair carboxylate reagents containing its own (hence Eigenbase) endogenous anionic base. The Eigenbase reagents work in the absence of added bases and this process is hinged on the interplay of structure and function of phosphonium carboxylate ion pairs in different solvents. The olefinations furnish a range of alkenes in high yields, no protecting groups are needed.
Third, most Eigenbase reagents can be prepared directly from alcohols as shown in route 1. This variant termed acidic stoichiometric olefination reaction (SORE) avoids use of halogen derivatives, bases and metal salts altogether.
Fourth, we went a step further and have achieved shortcut catalytic cycle 2 and develop cycle 3. These circular olefination reactions (CORE) are single-step catalytic protocols. The venerable Wittig-type olefination is done without phosphorus waste, protecting groups, organic halides, metal salts and bases. Ironically, it is now acid-catalysed.
Project Aims: identify set of conditions for preparation of new MOM-derived \Eigenbase reagents; explore new classes of R1 for example alkoxy-derivatives. You will learn: air-free wet chemistry ( O2 and water); detailed NMR characterisation: 1H, 13C, 31P Isolation and purification; Intro-level DFT computations.
This project aims to study the daily number of deaths from COVID-19 in the United States and Ireland based on the integer-valued nonlinear autoregressive models recently introduced by Barreto-Souza et al. (2023). The non-stationarity of the time series will be accommodated through the inclusion of covariates such as the number of patients hospitalized for the disease. Irish COVID-19 data has been analyzed, for example, by Barreto-Souza et al. (2022) based on a time-varying dispersion integer-valued Generalized AutoRegressive Conditional Heteroskedasticity process.
The project centers on working on novel fluorescence-based medical assay diagnostics. The project involves the student learning about assay biochemistry, and applying this to create assays on nanomaterials to study the potential of such nanomaterials to enhance the fluorescence signal and thus biomarker detection efficiency beyond that achieved using a conventional well plate. The student will work with material scientists who developed the nanomaterials and optical scientists to aid in the fluorescence measurements. We are looking for a student who has a background in biosciences with an interest in medical assay technology.
The project centers on understanding the potential of photochemistry to control chemical reactions under flow. The project involves working with chemists, material scientists, and physicists to study the impact of light and nanomaterials on chemical reaction processes. The student will work on optimizing reactor design and study a specific reaction. We are looking for a student who has a background in engineering or physical science with an interest in chemical reaction control and chemical reactor design and processes.
The Johnson group has expertise in the fabrication of nanopore structures and the is
interested in investigating the confinement of molecules within these structures. The
transport of ions, as well as other fundamental chemical properties, can be quite different
when confined to the nanoscale as opposed to the bulk. Typical projects will involve the
fabrication of nanopores and their characterization, followed by the development of a
sensing system for trace analytes of interest. Target analytes range from chemical and
biological contaminants in foods and medicine through to toxic ions in the environment. Our
research is highly interdisciplinary, with past students in biology, chemistry, physics and
chemical engineering all enjoying successful placements within the research group. Projects
are tailored to the student’s interests, but typically involve learning/developing some of the
Recent advancements in Virtual Reality (VR), sensor technologies, and wireless communication have made Extended Reality (XR) a possible mode of service for streaming and real-world applications. End users who utilize the XR services may require real-time sensing and localized decision-making via Machine Learning (ML) models deployed at the user device. To train such ML models, service providers can implement FL as a privacy- preserved distributed ML technique. However, ML models trained via plain FL have many privacy issues as these ML models tend to memorize patterns from client data that can be sensitive and private. The information leaked via XR-based services can be used by adversaries to launch assaults on the virtual experience of the users. For instance, an attacker can eavesdrop on model updates transferred via FL continuously to identify user preferences  and create personalized backdoors to trigger a worse user experience in a mixed reality service based on the identified preferences and habits of the target user exposed by the models. Therefore, in this project, we aim to identify such potential privacy issues practically emerging from ML models trained via VR devices and implement privacy risk assessment techniques and defense mechanisms to mitigate such privacy threats. We evaluate the practical trade-offs that can be possible with these privacy attacks and their feasibility in mitigating them at the device level.
Key learning outcomes involves:
Resources required include:
Sandeepa, C., Wang, S. and Liyanage, M., 2023, June. Privacy of the Metaverse: Current Issues, AI Attacks, and Possible Solutions. In 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom) (pp. 234-241). IEEE.
The following information is vetted and provided by the American Association of Collegiate Registrars and Admissions Officers (AACRAO) on the Electronic Database for Global Education (EDGE).
|Letter Grade||Percentage||Ranking||U.S. Equivalent|
|A+/A/A-||70 - 100%||First Class Honours||A|
|B+/B/B-||60 - 69%||Second Class Honours Upper||B+|
|C+/C/C-||50 - 59%||Second Class Honours Lower||B|
|D+||45 - 49%||Third Class Honours||C+|
|D/D-||40 - 44%||Pass||C|
|F||0 - 39%||Fail||F|