You will earn 6 research credits over 6 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.
Biomechanics, Biomedical Engineering/Chemistry, Chemical Engineering/Biochemistry, Chemical Engineering/Environmental Science, Chemical Engineering/Pharmacology, Chemistry, Civil Engineering, Computer Engineering, Computer Science/Cybersecurity, Data Science/Statistics, Food Science, Genomics, Mechanical Engineering, Neuroscience/Cell and Molecular Biology
| Course ID | Title | Credits | Syllabus |
|---|---|---|---|
| LIME RSLW 392S | International Independent Research in STEM Fields | 6 |
Background: Concussion awareness is increasing almost daily in most mainstream sports. Concussion is one of the mildest forms of brain damage. However, it is this mildness of injury which makes it one of the most insidious, as repeated and undetected concussions can lead to permanently altered brain function. There is currently no scientific test for concussion – only a subjective assessment. The meninges is a series of membranes that envelops the brain to protect it during impact. The purpose of this project is to mechanically characterise this membrane using, uniaxial, biaxial and fracture toughness techniques, along with electron microscopy. The project will also apply an injury mimicking concussion on the brain and comparing the effect of concussion on mechanical and structural properties of the tissue.
Scientific hypothesis being tested: Are the mechanical properties of the meninges location dependent within the brain? What affect would this have on location specific concussive impacts, and cortical protection design?
Background that the student needs to have: The students should have a lot of knowledge and experience in mechanical characterisation experiments, and hyper/linear elastic stress analysis. The students should have a basic background in biology.
Analytical techniques to be employed: Uniaxial testing of porcine tissue, stress/strain analysis, statistics, electron microscopy (will provide training)
Keywords: soft tissue biomechanics, concussion
Background: Carbon quantum dots (CQD) are a new generation of carbon nanomaterials with diameters below 10 nm, with amorphous or graphene-like carbon structures with excellent water dispersibility and adjustable excitation and emission wavelengths. They are generally derived from petroleum-based carbon precursors. Lignin, an aromatic biopolymer derived from wood, is an excellent candidate for the development of quantum dots. This project aims to develop quantum dots from technical and locally extracted lignin and test the fluorescence response of the CQD to light stimuli. Complementary, CQD will also be tested for biocompatibility.
Scientific hypothesis being tested: Lignin source and extraction has a strong influence on the physical-chemical properties of the biopolymer. This project aims to validate the importance of lignin sources on the quality of the CQD materials.
Background that the student needs to have: Biomedical Engineering, Organic Chemistry, Biology, Laboratory Work Experience; Data Analysis
Analytical techniques to be employed: FTIR, UV/Vis spectroscopy, SEM for imaging, Mammalian cell culture techniques, and Cytocompatibility/viability assay protocols.
Keywords: biomedical engineering, organic chemistry, biology
Background: The main objective of this research topic will be to determine the energy potential of solid (hydrochars) and liquid (liquors) products of hydrothermal carbonization (HTC) obtained in high-pressure chemical reactors in a variable pH environment. For this purpose, doses of a commercial acid catalyst will be selected for digested sludge, with a constant initial moisture content, under established reactor operating conditions: temperature and residence time. In addition to conducting HTC experiments, during the training the student will learn to perform the proximate and ultimate analyses of solid residue and liquid after HTC as well as determining a higher heating value by calorimetric technique, biomethane potential test and operation and analysis by gas-liquid chromatograph.
Scientific hypothesis being tested: HTC technique should be implemented to every waste water treatment plant
Background that the student needs to have: Completed first year science or engineering course.
Analytical techniques to be employed: gas-liquid chromatograph, biomethane potential test analyses, calorimetric bomb
Keywords: biochemistry, environmental science, chemical engineering
Background: Phosphorous (P) is essential for life, but it is a finite resource. The industrialization of food production in order to feed a rapidly expanding population is giving rise to serious leakage of P through the global agricultural food system. This is particularly pertinent in the dairy industry, where losses of P are causing environmental damage and ultimately putting food safety at risk. Hydrothermal carbonization (HTC) undertaken at moderate temperatures (160-315℃), and pressures up to 15MPa is a qualifying ‘strubias’ technology, typically used for treating wet wastes producing a hydrochar and a liquor. In the project, we investigate the effects of process parameters on P and carbon recovery from dairy process waste streams. We will also provide a detailed analysis of P solubilization and precipitation as recovered phosphate salts in a form of struvit (fertilizer).
Scientific hypothesis being tested: determination of optimal conditions for fertiliser (struvite) crystalization
Background that the student needs to have: Completed first year science or engineering course.
Analytical techniques to be employed: focused beam reflectance measurement of crystal grow, XRD
Keywords: chemistry, environmental science, chemical engineering
Background: The decomposition of biomass derived formic acid is finding an increased prominence, as we seek a sustainable source of molecular hydrogen for hydrogenating biomass platform compounds into advanced biofuels and biosolvents. Producing hydrogen from formic acid has led to appreciable improvements in the light of cost-effective green process. Formic acid is produced along with levulinc acid in an equimolar ratio by the hydrolysis of lignocellulosic biomass under acidic conditions. The low cost, abundance, and ability to hold molecular hydrogen up to 4.4 wt.% of formic acid, are an added advantage. Exploiting the in situ produced hydrogen enables us to avoid the use of expensive external source of molecular hydrogen in addition to its transport handling. Our research group has its main focus on developing a novel heterogeneous catalyst of a low cost, enabling us to decompose formic acid while hydrogenating levulinic acid, where this adds to sustainability, circular utilization of catalyst as well as more economic atomic steps.
Scientific hypothesis being tested: Our approach is problem-oriented and we seek to maintain metal active sites that would have dehydrogenation/hydrogenation potential. Therefore, we propose co-doping process as a further step while investigating its effect on both conversion and selectivity.
Background that the student needs to have: Completed first year science or engineering course.
Analytical techniques to be employed: HPLC-UV
Keywords: chemical engineering, analytical chemistry, environmental science
Background: Meat and bone meal ashes (MBMA) obtained from fluidized bed combustion are rich in phosphorus content. The aim is to create possible processing of MBMA, whereby phosphorus can be efficiently extracted to make phosphate fertilizer. MBMA will be leached with different concentrations of sulphuric acid using various liquid-to-solid ratios, resulting in phosphoric acid recovery. Other mineral and organic acids will be used to compare their effectiveness. The leaching time and pH required for high phosphorus dissolution will be determined. Obtained phosphoric acid can be then precipitated to phosphate compounds. This can be achieved in different ways, for example by adding lime water. The optimal conditions for those processes need to be examined.
Scientific hypothesis being tested: Our approach is to make phosphor reach fertiliser from ash.
Background that the student needs to have: Completed first year science or engineering course.
Analytical techniques to be employed: The spectrometric method will be used for phosphate analysis. The heavy metals will be analysed by ICP-OES. Characterisation of crystal morphology will be conducted using SEM and XRD.
Keywords: chemical engineering, analytical chemistry, environmental science
Background: Willow (Salix spp.) bark is an incredible source of compounds with pharmaceutical properties, and it has been used for centuries as a natural remedy against inflammation and fever. Since willow wood is used for other applications (combustion, biofuels, biomaterials), the debarking of willow trees is a crucial step to be able to recover high-value compounds from the bark. Starting with bark material from several willow clones, the solvent extraction (assessing different solvent systems) and then subsequently the chromatographic and spectroscopic analysis of the bark extracts, the optimal column chromatography purification method will be developed to target a mix of high-value pharmaceutical constituents (e.g. salicin, salicortin, catechin) present in the extracts. During this time, several extraction techniques (accelerated solvent extraction, Soxhlet) and analytical techniques (HPLC-UV, LC-MS-QToF, GC-FID) will be employed to obtain exhaustive information on the chemical composition of the samples in terms of non-structural components that can be extracted from the plant material.
Scientific hypothesis being tested: Recover of high-value compounds from a Willow bark.
Background that the student needs to have: Completed first year science or engineering course.
Analytical techniques to be employed: HPLC-UV, LC-MS-QToF, GC-FID
Keywords: biochemistry, analytical chemistry, environmental science, chemical engineering, pharmacy
Background: Transmission electron microscopy (TEM) is an analysis technique unlike a conventional microscope, where electrons are used to view very small samples (in the nanometre range) in a high vacuum environment like what would be expected in space. An extension of this technique is called liquid phase TEM where specialised sample holders are designed to allow observation of liquid samples on the nanometre scale. This particular technique allows observation of nanomaterial dynamics such as Brownian motion, electrochemical reactions and nucleation. In the pharmaceutical industry, a thorough understanding of the crystallisation process and propensity for uncontrolled polymorph transition is required to fully characterise an API and assess the most beneficial crystal structure for treatment that will have optimum bioavailability and dosage. We can utilise LPEM to observe in situ the nucleation of an API on the nanoscale to discover the poorly understood events of the preceding and intermediate stages that lead to the formation of API crystals.
Scientific hypothesis being tested: One of the most challenging aspects of LPTEM is the interaction between the electron beam and the sample itself as the liquid will undergo radiolysis in different forms to product gaseous, molecular, ionic, and radical products that will change the local chemistry of the sample being analysed. In previous experiments by Cookman et al., LPTEM was used to manipulate the crystallisation process to produce 1 out of a possible 9 crystal forms in extreme undersaturation (250x under saturation) which, in conventional experiments is impossible. The main hypothesis being probed is that the radiolysis products are allowing this unique crystallisation process to occur, however more information is required to understand specifically how, and in what proportion. This project will use an existing platform to simulate the radiolysis products of simple solvent systems, their steady state concentrations, their residence in the area of the electron beam and beyond that into the wider reaction area.
Background that the student needs to have: Knowledge on programming using python, use of anaconda and jupyter notebook. An interest in pharmaceutical research and impacting global health. Non-compulsory backgrounds but beneficial - General chemistry, radiation chemistry, pharmaceutical chemistry, solvent-solute interactions.
Analytical techniques to be employed: Simulations on radiolysis breakdown of simple solvent systems. Assessment of steady state concentrations of radiolysis products. Radiolysis product residence time and lifetime. Assessment of temperature influence due to the electron beam.
Keywords: radiation chemistry, computer programming, computational physics, computational chemistry, pharmaceutical science, electron microscopy
Background: Gridshells are composed of assemblies of prismatic linear elements shaped into a shell-like form. Gridshells may be assembled from multiple layers locked together with shear blocks. The stiffness of multi-layer gridshells is often modelled using a single equivalent stiffness parameter. Research at UL (Collins 2016; Mellad et al. 2018; Collins and Cosgrove 2019) has shown that this approach cannot take account of secondary bending that occurs in individual layers between shear blocks. Secondary bending occurs when locally slender elements undergo local bending in response to shear forces. The relative importance of secondary bending has been investigated for flat double-layer assemblies. This Arcadia project addresses secondary bending in curved double-layer assemblies using Finite Element Analysis (FEA).The research involves building a range of linear elastic, FEA models of curved double-layer single assemblies (not whole gridshells) of varying geometries.
Scientific hypothesis being tested: The project seeks to identify functional dependencies between the ratio of secondary to primary bending and assembly geometry. Geometrical attributes such as radius of curvature, shear block spacing, and solidity ratio will be examined. Ultimately, this research will provide guidance to gridshell designers on limiting geometries for which secondary bending is not significant.
Background that the student needs to have: The student would need to have completed standard undergraduate modules in the analysis of indeterminate structures and mechanics of materials and have some experience using FEA. While not essential, knowledge of Abaqus FEA software would allow more progress to be made in the available time.
Keywords: civil-structural engineering, gridshells, finite element analysis, structural engineering, secondary bending
Background: Security incidents targeting smart factories are becoming more common and can be harmful to assets, operations, and people. A forensic-ready system can facilitate a post-incident investigation by proactively identifying, collecting, and preserving data that can potentially be used as evidence. Data identification recognises sources from which data relevant to potential incidents should be collected. However, performing data identification manually can be error-prone, while collecting data unconditionally can result in large volumes of data, hampering subsequent investigations. In this project, we work on developing a semi-automated approach to perform data identification for forensic-ready smart factories. With pragmatic smart factory applications in mind, we will develop templates to model data sources and use them to represent the specific data sources that are available in a factory (such as on-robot sensors).
Scientific hypothesis being tested: How to engineer more forensic-ready smart factories? The aim of the project is to develop a semi-automated approach to perform efficient and effective data identification as part of the forensic readiness activities of a smart factory.
Background that the student needs to have: Background in Computer Science, Software Engineering or related field. Good programming skills (in Java or Python; other languages could work). Basic knowledge in cybersecurity and cyber-physical systems is preferred.
Analytical techniques to be employed: As part of the project’s team, you will be working on creating a catalogue of data sources, which can be found in a smart factory (such as autonomous robot sensors). This will be useful for developing our approach in this project; for other researchers to use the catalogue; and for you to gain knowledge on smart factories, their components, and their use for security and digital forensics. Creating a catalogue could be done via reviewing and collating existing resources on smart factories, related security incidents, and by getting a hands-on experience simulating smart factories using various tools such as Siemens Tecnomatix Plant Simulation tool. The creation of a catalogue will also involve the development of a tool to allow us and other researchers to view and use it. To this end, you will need to apply your programming skills to develop the catalogue, and possibly linking it to existing platforms.
Keywords: software engineering, digital forensics, Industry 4.0
Background: Kindness can increase happiness and wellbeing. It has benefits for individuals (such as decreasing anxiety, increasing resilience) and for society (such as increasing trust). With digital technology permeating our daily lives, there are increasing opportunities for such technology to enable, mediate, and amplify kindness in society. In this project, we propose kind computing, which is a new computing paradigm that explicitly incorporates kindness into the development and use of digital technology. We envisage software engineering (SE) as a discipline that can deliver such technology. However, software engineering techniques do not provide explicit abstractions, formalisms, and tools to consider, analyse, and implement software that delivers such technology.
Scientific hypothesis being tested: How can we engineer kindness into software? The aim of the project is to explore ways and applications to realise kind computing. To this end, we are investigating approaches to represent kindness in the world and provide tools for software engineers to model and reason about it in their systems.
Background that the student needs to have: Background in Computer Science, Software Engineering or related field. Good programming skills (in Python; other languages can be considered). Basic knowledge in front-end web development (JavaScript, CSS, HTML) is a plus. Basic knowledge in Human Factor design is also a plus.
Analytical techniques to be employed: Joining our project, you will be part of a team working on creating novel representations of kindness. Specifically, you will work on developing a tool that can capture entities and relations of kindness and provide graphical representations of them. The tool should allow SE researchers to create new representations of acts of kindness, view existing ones, and perform various tasks (such as editing and deleting). For demonstration purposes, it could be developed as an online tool using a combination of Python (Flask/Django for backend development) and JavaScript (for frontend development); however, other programming languages and platforms can also be considered (such as the Eclipse Modelling Framework). The tool will be the first of its kind to allow SE researchers to represent (and eventually) enable kindness facilitated by software. So, you can be part of the team shaping this tool and the future of kind computing!
Keywords: software engineering, human-centred computing, social psychology
Background: Unmanned Aerial Vehicles (UAVs) are gaining popularity due to smart and intelligent data collection processes. Nodes mobility and uncertainty make these network vulnerable and easy target for attackers. The UAVs network services can be compromised or interrupted by using DoS, jamming, black hole, worm and sinkhole, floods, and sybil attacks. Data security has a direct connection with trust value of neighbor nodes. Secure data communication and trustworthy devices are the fundamental and essential requirements of these networks.
Scientific hypothesis being tested: This project will investigate the security issues and attacks behavior of UAV nodes in data collection processes. Project will outline the secure and trust based solutions for UAV-to-UAV, UAV-to-Ground Networks and UAV-to-Infrastructure networks.
Background that the student needs to have: The student should have a basic background in data communications and security.
Analytical techniques to be employed: Attacks models usage and trust and security provision methods to secure the UAVs networks and make the nodes trustworthy in the network.
Keywords: computer engineering, science, security
Background: Over the years, cybersecurity standards and frameworks have been designed for providing data privacy and security methods to ensure safe transfer of data. However, with emerging technologies the cyber threat landscape has changed and led to increased cyber attacks and breaches. The reason is even the best methods need to be aligned in scope of the changing threat vectors. This research project focuses on understanding and aligning existing cybersecurity regulatory frameworks (GDPR, Californian Consumer Privacy Act (CCPA), Colorado Privacy Act, ADPPA) for safe transfer of sensitive/personal information (i.e. healthcare data, genomic data) data flow across trans-Atlantic (U.S.to EUand vice-versa) borders mitigating data security and privacy issues.
Scientific hypothesis being tested: Is it possible to achieve data security and privacy with existing cybersecurity regulatory frameworks?
Background that the student needs to have: The project is based on review research. There is no pre-requisite requirement.
Keywords: cybersecurity, data security and privacy, standards, regulations
Background: Smart home is one of the areas of IoT networks where the home appliances are connected with internet and smart grids. However, these networks are at high risk in terms of security violations. Different kind of attacks have been conducted on these networks where the user lost their data. Intrusion Detection Systems (IDS) are used to detect and prevent the cyber-attacks. These systems are based on machine and deep learning techniques and still suffered with under fitting or overfitting issues.
Scientific hypothesis being tested: This project investigates the security issues in smart home networks. The anomaly based intrusion detection for smart home networks will also investigate to address the overfitting/under fitting issues and ensure the high performance in terms of hybridization.
Background that the student needs to have: The student should have a basic background in data communications and security.
Analytical techniques to be employed: Machine and deep learning approaches and how these are used in IDS and IPS systems to secure the smart home networks.
Keywords: computer engineering, science, security
Background: Cyber range is used to provide hands-on experience in the field of cybersecurity. A cyber range is an interactive technology environment where student and professionals of cybersecurity can learn about how to detect and mitigate cyber-attacks.
Scientific hypothesis being tested: In this 6-week project, student will be using CYRIN cyber range. Student will be given exercises to learn about cyber-attacks and the techniques to mitigate the attacks. Student will also learn the cybersecurity tools used by cybersecurity professionals/attackers.
Background that the student needs to have: Student should have basic theoretical understanding of cybersecurity
Analytical techniques to be employed: Student will write a report on the given exercises.
Keywords: cybersecurity, cyber-attacks
Background: Cyber forensics involves understanding threat patterns, adversarial behaviour tactics, techniques and procedures (TTP) (e.g. How did an intruder enter the network? How were the vulnerabilities identified? Which resources were exploited? How did the threat escalate? Was it an insider threat, etc.). The MITRE ATT&CK framework provides a logical map for identifying and understanding malicious threat patterns and scenarios. A robust cyber defence strategy for protecting an organisation can only be developed if the intent and capability of a malicious actor is known. The aim of this project is to gather cyber threat intelligence and mitigate cyber threats in healthcare using the MITRE ATT&CK Framework.
Scientific hypothesis being tested: Is Threat Intelligence the only way to mitigate escalating threat vectors and patterns?
Background that the student needs to have: Networking concepts
Analytical techniques to be employed: MITRE ATT&CK tool
Keywords: cyber defense, threat intelligence, forensics, MITRE ATT&CK framework
Background: Genomic data refers to the genome and DNA of an organism – an emerging field in data science, where researchers use bioinformatics for storing, collecting, analysing DNA sequences. The main goal of genomics is to find treatments for complex diseases (e.g. Cancer, diabetes, heart conditions, etc), employs huge volume of data for analysing specialised analytical, computational tools and technological/cloud dependencies. The National Genome Research Institute anticipates more than 2 exabytes of data to be generated in the coming years. Such a landscape will bring more cyber risks (i.e. genetic hacking) at stake as 99% of human share the same genome. The aim of the project is to secure genomic/sensitive data processed using analytical and cloud services based in different jurisdictions and find potential cybersecurity and regulatory methods to protect sensitive data.
Scientific hypothesis being tested: Genomic data, if breached can be employed for profiling specific people with a malicious intent.
Background that the student needs to have: The project is based on review research. There is no pre-requisite requirement.
Keywords: cybersecurity, genomic data, privacy, standards, regulations
Background: Cyber attackers are targeting medical devices, specifically consumer health monitoring systems, wearable, embedded and stationary devices. There have long been vulnerabilities within many medical devices, but awareness of these vulnerabilities has risen as a result of a recent increase in cyberattacks. Most attackers are interested in stealing large amounts of patient data/medical records, which they see as more valuable than credit card data. The issue is that legacy medical devices do not support modern security solutions; this makes them vulnerable to cyber-attacks.
Scientific hypothesis being tested: Legacy medical devices contain outdated software as they do not receive manufacturer support for updates. This project will focus on reviewing existing security solutions for legacy medical devices. The student will complete a literature review and write a report.
Background that the student needs to have: Student should have basic understanding of cryptography/security.
Keywords: cybersecurity, cyberattacks, medical devices, legacy devices
Background: Neural networks pervade modern society, and are now making decisions in a wide range of scenarios such as medical diagnosis, fraud detection, self-driving cars, and industrial process modelling, among many others. However, there is growing concern that these models lack statistical development. Consequently, they tend to be overly complex and may overfit data. On the other hand, while balancing data fit and model complexity is a core aspect of statistical modelling, neural networks have not historically been developed from a statistical perspective. Therefore, the aim of this project is to take a statistical modelling approach to neural networks by applying a stepwise information-criteria-based input- and hidden-neuron selection procedure. Using both real and simulated data, the neural networks selected using this procedure will be compared to more complex networks that lack selection.
Scientific hypothesis being tested: Neural networks can be simplified using statistical procedures without any significant loss in prediction performance.
Background that the student needs to have: The student should have a quantitative background, for example, in statistics, data science, or computer science. Some background in either neural networks or statistical model selection procedures would be helpful but is not necessary (and it is unlikely that any student would have advanced knowledge of either of these areas).
Analytical techniques to be employed: statistical stepwise selection, neural networks
Keywords: information criteria, model selection, neural networks, statistics
Background: Health effect evaluation and development of new natural low-calorie sweeteners (LCS) are strategies to tackle non-communicable diseases (SDG 3). Advances in our understanding of the human body highlighted the critical role of the gut microbiome in our homeostasis and health. LCS are regulated food additives that used to be evaluated only with traditional toxicological methods. Public opinion and the EFSA have been demanding new studies on LCS to evaluate their impact on health and the gut microbiome. Gut microorganisms communicate within their community and regulate group behaviors via a molecular system termed quorum sensing (QS). This interdisciplinary project aims to elucidate the impact of two natural origin LCS on this bacterial communication system, evaluated comparatively using in vitro models (biosensor assays, swarming motility assays, growth assays) to gain mechanistic insights, as well as molecular docking.
Scientific hypothesis being tested: Evaluation of mechanistic aspects of LCS interaction in bacterial models. Novel insights into microbial communication affected by LCS will bring mechanistic elucidations and could explain contradictory results in previous literature.
Background that the student needs to have: Basic microbiology skills are necessary Bioinformatics skills are desirable.
Analytical techniques to be employed: Bench lab techniques: basic microbiological skills (media preparation, bacteria growing, culturing, identification), measurements using Spectrophotometer, PHmeter, titration. Bioinformatics tools : molecular docking
Keywords: natural sweeteners, microbiome, microbial quorum sensing, bioinformatics, molecular docking
Background: In 2019 and 2020, 75,836 million metric tonnes of apples were processed, where apple pomace (AP) accounted for approximately 25%. AP is composed of skin and flesh (95%), seeds (2%–4%), and stems (1%). In Ireland, the production of apples is estimated at 45,000 tonnes, which generates roughly 11,250 tonnes of AP. Although AP is a source of fibre (14-47% dry basis) and phenolic compounds (2% dry basis), it is considered waste by the industry with an associated disposal cost. AP contains several phenolic compounds, of which phlorizin, (-)-epicatechin, procyanidin B2, 5-caffeoylquinic acid, and quercetin-3-galactoside make up 75% (Barreira et al., 2019). These phenolics may be recovered by water extraction, a green alternative to organic solvents. Extraction and purification of APP, eliminating sugars and organic acids, results in a fibre-rich fraction (APF). In this project, we will use different resins to purify AP and obtain a polyphenol-rich fraction to be further used as an ingredient in the food industry.
Scientific hypothesis being tested: The purification of AP extract will enhance the polyphenol content in the extract and increase its biological activity.
Background that the student needs to have: analytical chemistry, organic chemistry
Analytical techniques to be employed: HPLC, UV-Vis spectrophotometry
Keywords: polyphenols, antioxidant capacity, HPLC, biocircular economy
Background: Our group is interested in isolating and identifying functional polar lipids of food origin that have strong anti-inflammatory activities, i.e. the potential to stop the onset of inflammation and thus the development of CardioVascular Diseases (CVDs). These lipids can be further exploited in various ways, ranging from developing novel health claims (according to EFSA EU guidelines) for foods till producing novel neutraceuticals and pharmaceuticals.
*(Lordan and Zabetakis 2017; Megalemou et al. 2017; Sioriki et al. 2016)
Scientific hypothesis being tested: The bioactivities of food lipids to inhibit the aggregation of platelets in humans and hence inhibit inflammation and the onset of cardiovascular diseases.
Background that the student needs to have: Food Science or Biochemistry or Chemistry
Analytical techniques to be employed: GC/MS, platelet aggregometry, lipid extraction techniques
Keywords: cardiovascular diseases, inflammation, platelets, dairy food, lipids
Background: Current approaches for detecting exonic boundaries in genomic sequences rely on scanning a potential splice site acceptor or donor against a profile of splice site acceptor or donor sites. These profiles have been generated by extracting splice site sequences from spliced or mature mRNA sequences from sequencing Expressed Sequence Tags (ESTs) as well as mature mRNAs. The proposed research will explore if the use of machine learning, and in particular Deep Learning algorithm can improve the detection of splice sites in genomic sequences and therefore enhance our understanding of transcript structure and potentially novel exons.
Scientific hypothesis being tested: Improved detection of exon-boundaries in genomic sequences using Deep Learning and therefore, identification of new exons.
Background that the student needs to have: Background in genomics, bioinformatics and programming experience preferably in Python
Analytical techniques to be employed: Collecting true positive and true negative data from genome sequence databases, building models for splice sites using existing Python libraries and predicting splice sites for data that was not used for training/model building.
Keywords: genomics, bioinformatics, computational biology
Background: Fused deposition modelling (FDM) 3D printing is a low-cost prototyping technique that can be used to manufacture a variety of materials. 3D printing enables “complexity for free,” meaning prototypes can be manufactured with complex internal structures. Using FDM 3D printing of thermoplastic polyurethane (TPU), there is an opportunity to design and develop tough impact protection (Personal Protective Equipment (PPE)) for motorcycle armour with improved energy absorption ability over standard off-the-shelf impact protections. The aim of this project is to investigate the opportunities for FDM 3D printing of TPU for the manufacture of motorcycle PPE.
Scientific hypothesis being tested: 3D printed motorcycle impact protection with controlled internal structures can absorb more energy than off-the-shelf impact protection
Background that the student needs to have: FDM 3D Printing experience essential. Materials engineering an advantage.
Analytical techniques to be employed: 3D Modelling. 3D Printing. Mechanical testing. Impact testing.
Background: Digital twins in manufacturing are real-time digital objects of physical manufacturing machines, which help optimise production efficiency. This project looks at developing and refining a metal bending testbed and associated digital twin to improve the manufacturing efficacy. The testbed uses FEA simulations to make predictions about the manufacturing process, which will be used to control the process in real time for improved efficiency.
Scientific hypothesis being tested: Digital twin technology can be used for process prediction to improve the manufacturing efficiency of metal bending
Background that the student needs to have: Microcontroller programming (Arduino, Raspberry Pi, ESP32), Python/C Programming
Analytical techniques to be employed: Design of experiments. Physical metal bending, measuring specimens.
Background: The Confirm Team at University of Limerick is developing a Digital Twin of an automated robotic drilling process. As part of this work, data are extracted from the drilling tool during the drilling process (e.g. torque, feed rate, tool position). The goal of this work is to relate these data to important features of the drilling process, e.g. material removal rate, tool wear, and hole quality.
Scientific hypothesis being tested: Can we develop a digital twin of an automated drilling robot to monitor and optimise the process without the need for user intervention?
Background that the student needs to have: Engineering materials, stress analysis, programming (Python or MATLAB)
Analytical techniques to be employed: Data smoothing and analysis; optimisation; machine learning (time permitting).
Keywords: mechanical behaviour of materials, data analysis, automated manufacturing, drilling.
Background: Astrocytes in the brain are key cells, which carry out a response to central nervous system pathologies. However, too much of anything is never good; hence, over activation of astrocytes can negatively impact CNS physiology, for example, through altering nerve cell connections. As a result, astrocytes have been linked to both neurodegenerative and neurodevelopmental diseases such as Alzheimer’s disease and autism spectrum disorder. Interestingly, astrocytes seem to become overreactive in response to abnormal levels of certain trace metals.The platelet activation factor receptor (PAFr) is expressed in astrocytes. Its activation results in a cascade of inflammatory signalling, activating astrocytes. Previous work carried out by our group has shown that PAFr signaling is influenced by the metal zinc (and other modifiable nutritional factors). Therefore, we will investigate whether the presence of toxic metals like lead or mercury may act on this signaling pathway by competition with zinc.
Scientific hypothesis being tested: Is PAFr protein expression altered in astrocytes when subjected to excess and toxic trace metals? Does the activation state of astrocytes correspond to altered PAFr expression? Does the altered expression PAFr and activation of astrocytes leads to altered neuronal development? Is the involved mechanism competition with the essential trace metal zinc?
Background that the student needs to have: Students should have a sound background in Cell Biology, Biochemistry or other Life Sciences, preferably Neuroscience, with a particular interest in inflammation and its role in the brain. Practical experience in cell culture techniques is preferred but not necessary, as full training will be provided.
Analytical techniques to be employed: Cell cultivation, Immunocytochemistry, Fluorescent microscopy, image analysis, Protein expression analysis (Western blotting), Gene expression analysis (qRT-PCR), and statistics.
Keywords: neuroscience, cell biology, bioscience, neuroinflammation, trace metals
Background: Security incidents targeting smart factories are becoming more common and can be harmful to assets, operations, and people. A forensic-ready system can facilitate a post-incident investigation by proactively identifying, collecting and preserving data that can potentially be used as evidence. Data identification recognises sources from which data relevant to potential incidents should be collected. However, performing data identification manually can be error-prone, while collecting data unconditionally can result in large volumes of data, hampering subsequent investigations. In this project, we work on developing a semi-automated approach to perform data identification for forensic-ready smart factories. With pragmatic smart factory applications in mind, we will develop templates to model data sources and use them to represent the specific data sources that are available in a factory (such as on-robot sensors).
Scientific hypothesis being tested: Does the gene knockout affect the susceptibility of the bacteria to a particular antibiotic?
Background that the student needs to have: A background in bioinformatics and/or experimental microbiology is desirable, but not required.
Analytical techniques to be employed: Bioinformatics software (BowTie2, HTSeq, DeSeq2), microbial cell culture, MIC determination
Keywords: Biological engineering, microbiology, bioinformatics, antimicrobial resistance
Background: Antimicrobial resistance is one of the defining healthcare challenges of the 21st century, and new antibiotics that can circumvent the innate defence mechanisms of drug-resistant bacteria are urgently needed. Computational and systems biology techniques have previously been applied to large-scale biological datasets, revealing novel targets for a wide range of difficult-to-treat diseases. These same techniques can also be extended to the study of bacteria to identify potential resistance factors, or targets for a novel antimicrobial therapy. This multidisciplinary research project will employ an array of bioinformatics software and machine learning-based algorithms to profile the proteome of Klebsiella pneumoniae. This analysis, combined with the transcriptome profiling of literature-derived data, will be used to identify novel drug targets. Carbapenem resistant K. pneumoniae (CRKP) is highly prioritised by the world health organisation (WHO) given its role in the acquisition of healthcare-associated infections.
Scientific hypothesis being tested: Can bioinformatics be used to identify novel drug targets in Klebsiella pneumoniae?
Background that the student needs to have: Previous experience with bioinformatics, and an introductory background in biology is desirable, but not required.
Analytical techniques to be employed: Bioinformatics software (BowTie2, HTSeq, DeSeq2), programming languages (Python, R)
Keywords: Bioinformatics, computational biology, systems biology, antimicrobial resistance
Background: Antimicrobial resistance is one of the defining healthcare challenges of the 21st century, and new antibiotics that can circumvent the innate defence mechanisms of drug-resistant bacteria are urgently needed. A previous bioinformatics analysis has identified several proteins of interest in the bacteria Escherichia coli with potential as targets for a novel antimicrobial therapy. Genetically engineered knockout (KO) studies are commonly employed in industry to validate potential drug targets. This project will investigate the antimicrobial susceptibility and biofilm formation propensity of a bacterial KO cell line. Microbiology assays will be used to quantify differences between resistant, wild-type and KO cells, providing an important mechanistic insight into the emergence of antimicrobial resistance.
Scientific hypothesis being tested: Does the gene knockout affect the susceptibility of the bacteria to a particular antibiotic?
Background that the student needs to have: A background in experimental microbiology is desirable, but not required.
Analytical techniques to be employed: Microbial cell culture, MIC determination, biofilm formation assays (crystal violet staining)
Keywords: Biological engineering, microbiology, antimicrobial resistance
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 |