College of Science and Engineering

Dr. Karl Mason 

AI, Machine Learning, Multi-Agent Systems, Reinforcement Learning, Evolutionary Computing, Robotics, Applied AI. 

Collaboration in multi-robot systems.

Machine learning, simulation, robotics, hardware

Dr. Patrick Mannion 

Multi-Objective Decision Making, Multi-Agent Systems, Reinforcement Learning, Optimisation, Game Theory 

Dr. Waqar Shahid Qureshi 

Artificial Intelligence, Computer Vision, Human Computer Interface, Precision Agriculture, Robotics 

In the Era of AI where humans will be working together with machine agents there will be dire need of understanding human intention modelling, which enables agents installed in robots to understand how humans perceive or intend to do. We like to explore such AI models in the context of intelligent manufacturing   

PhD in Computer Science or Robotics or related field 

Dr. Effirul Ramlan 

Biological computing, Molecular computing, Computing using nature-inspired novel substrates. 

Augmenting Foundational Models to Engineer Complex Supramolecular Systems for Nanotherapeutics 

PhD in Computer Science / Biochemistry with keen interest in exploring self-organised computing systems 

Dr. John McCrae 

Natural language processing, knowledge graphs, artificial intelligence, linked data, lexicography, digital humanities 

Projects related to the application of natural language processing techniques, especially large language models, to areas related to the construction of knowledge graphs, particularly with application to digital humanities areas 

Good knowledge of NLP and related technologies 

Dr. Bharathi Raja Chakravarthi 

Natural Language Processing 

Prof. Peter Corcoran 

Computational, Cognitive and Connected Imaging; Computer Vision; Neural Networks (Neural Algorithms, Training Methodologies, Data Augmentations, Generative Data (for Training)); Edge-AI; Neural Speech Processing, Child Speech Processing (text-to-speech and speech-to-text); Multi-modal Imaging (Thermal, NIR, LWIR, Event-Camera, Neuromorphic Vision, etc); Analysis of Human Subjects (Computer Vision, Speech). 

"Sensing without Seeing" - using Event Cameras to provide Security and Privacy when analysing human subjects for their cognitive load, emotional state and behaviours. Use cases in advance HCI (Human Computer Interaction); Training Data Synthesis for Event Cameras and use cases in Embedded Imaging (Neural Accelerators); Improved Child Speech Understanding and Speech Generation; Edge-AI (embedded) Child Speech for Smart Toy platform 

Image, Video, Speech Analysis; knowledge of state of art neural algorithms (vision transformers, diffusion models, etc); Working with Event Cameras or similar asynchronous data signals; work with spiking or sigma-delta NN or equivalent;  embedded programming and/or FPGA experience; Neural Speech experience, particularly in low-resource languages or equivalent; experience with training advanced neural models or complex training frameworks; experience with transfer learning and fine-tuning of advanced neural models/architectures. 

Dr. Pedram Masjedi 

Developing advanced numerical tools for the aero/hydro-elastic analysis of onshore and offshore wind turbines, as well as composite structures; Computational mechanics, especially meshless methods and Isogeometric Analysis (IGA); Wind Turbines, Aeroelasticity, Fluid Structure Interaction (FSI), Composite Structures, Computational Mechanics, Meshless Methods, Isogeometric Analysis (IGA) 

Strong skills in the mathematical modelling of solids and/or fluids, numerical methods and computer programming 

Dr. Pouyan Ghabezi 

Composites, Additive Manufacturing, 3D printing, Composite material characterization, Advanced manufacturing and testing of composites, Polymers recycling, FEM 

Dr. Muhammad Muhammad 

Mechanical metamaterials, acoustic metamaterials, elastic metastructures, phononic crystals, topological phononic metamaterials, vibration and acoustics, finite element analysis modelling, structural dynamics, solid and fluid mechanics  

Guided acoustic/elastic waves in nonreciprocal nonlinear media; Mechanical metamaterial based orthopaedic implants for biomedical applications 

Finite element analysis (COMSOL Multiphysics desirable), structural design and analysis, structural dynamics, wave propagation, mechanical tests 

Prof. Kathryn Cormican 

Technology innovation management; Responsible service innovation; Digital service innovation; Operational excellence; Project management 

Digital service innovation: Capability model and performance indicators 

Ability to synthesize and analyse literature, Model development, Advanced statistics e.g. partial least squares structured equation modelling (PLS SEM) Excellent writing skills 

Dr. Eimear Dolan 

Medical Implants; Soft Robotics; Active Implants; Immunotherapy; Drug Therapy; Ovarian Cancer; Diabetes.

Medical implants are placed inside the body for medical purposes, usually for long periods. Presently, implant failure is expected and inevitable, and the costs, inconvenience and morbidity that it can impose on patients are largely accepted. Once any foreign object is implanted it triggers a series of cellular events to protect the body, known as the foreign body response. This can lead to the formation of a dense fibrous capsule around the implant that becomes impermeable and can lead to implant failure, as it prevents integration of the implant with the body. This is particularly concerning for implants that rely on harmonious contact with the body, where seamless exchange of molecules is necessary for implant function.  Our research group have developed a novel platform which actuates (inflates and deflates in a controlled manner) to interfere with the foreign body response. We have shown that our novel actuatable platform can maintain therapy delivery for eight weeks. A fundamental understanding of the foreign body response over time is required to enable the design of strategies to overcome it. We will explore the longer-term performance of our actuatable implant and the delivery of drug and cell-based therapies through our therapeutic implants for diseases such as Ovarian Cancer and Diabetes.

PhD in Biomedical Engineering, Immunology (or related field);    Experience designing and conducting in vivo assessments OR demonstration of a strong commitment to up-skill; Strong track record in designing and executing in vitro assessments and molecular biology techniques; Demonstration of dynamic, creative thinker and problem solver interested in active therapeutic medical implants.

https://scholar.google.com/citations?user=VG1V1MEAAAAJ&hl=en

https://x.com/eimearbdolan?lang=en 

Prof. Abhay Pandit 

Biomaterials, Drug Delivery, Tissue Engineering, Biomaterial Synthesis 

Regeneration by Glyco-functionalisation of Biomaterial Systems 

Strong proven experience in molecular biology or biomaterial synthesis, histopathology, good writing skills, independent and proven track record in high impact journals 

Dr.  Xulian Qian

1. Bio-Nano Mechanics (Study of mechanical interactions between nanomaterials and biological systems, focusing on deformation, energetics, failure mechanisms, transport phenomena, and dynamic responses at the bio-nano interface);  2. Multi-Scale Computational Modeling (Development of computational frameworks spanning molecular to continuum-level simulations. Applications include biological tissues, implants, interfaces, and bio-nano systems, integrating molecular dynamics, finite element analysis, and machine learning); 3. Biomimetic Self-Assembling Nanomaterials (Design and engineering of programmable biomimetic nanomaterials that autonomously phase-separate, assemble, and reorganize into functional structures for biomedical applications, e.g., drug delivery, biosensing); Keywords: Bio-Nano Mechanics, Multi-Scale Modeling, Self-Assembly, Biomimetic Materials, Cell-Nanomaterial Interactions, Machine Learning, Biomedical Implants, Computational Biomechanics.

This project aims to advance the design of self-assembling nanomaterials for biomedical applications by integrating multi-scale computational modelling and experimental validation. The research will focus on understanding how nanomaterials interact with biological systems across spatial scales—from molecular-level self-assembly to tissue-level integration—with applications in targeted drug delivery and biocompatible implants. Using hybrid computational approaches (e.g., molecular dynamics, finite element analysis), we will simulate the mechanical behaviour of bio-nano systems in complex biological environments, validated by experimental data on nanomaterial-cell interactions. Key objectives include: (1) developing computational tools for multi-scale modelling of bio-nano systems, (2) identifying design principles for self-assembling nanomaterials, and (3) prototyping implant coatings with tunable mechanical properties. This interdisciplinary work bridges materials science, computational biology, and biomechanics, offering transformative insights into bio-nano mechanics while addressing critical challenges in personalized medicine and regenerative technologies.

Strong background in computational mechanics (e.g., molecular dynamics, finite element analysis); Experience with machine learning applied to materials science or biological systems; Knowledge of biomechanics or cell-material interactions; Proficiency in programming (e.g., Python, MATLAB, C++, Jupyter); Optional (Ideally a Plus): Familiarity with nanomaterial synthesis and characterization techniques (e.g., AFM, SEM, DLS).

1. Candidates with expertise in multi-scale modelling and ML-driven material design are especially encouraged to apply. 2. The lab prioritizes open science; contributions to open-source tools / codes/software are encouraged. 3. While the project focuses on computational and theoretical framework, collaboration with experimental partners will be supported.

Dr. Alma Siggins

Environmental Biotechnology; Bioremediation; Methane oxidation; Biofilm development.

Evaluation of methane oxidation capacity by a biochar immobilised microbial community. Flexibility around specifics to suit candidate but may include microscopy and analysis of biofilm formation, specific metabolic processes and products, or methane adsorption modelling.

Environmental microbiology, biotechnology, adsorption analysis and modelling, microscopy, biofilm formation analysis. 

Previous MSCA PF awardee 

Dr. Merve Suzan Zeden 

Antimicrobial Resistance, MRSA, ESKAPE Pathogens, Bacterial Metabolism, Signalling nucleotides 

Molecular Mechanisms of AMR in the ESKAPE Pathogens 

Molecular Bacteriology, Bioinformatics, Omics, Biochemistry 

Dr. Florence Abram

My research centres around the application of novel technologies, including omics and machine learning, to investigate the response of microorganisms and microbiomes to changing conditions. Keywords: Microbiology, Machine Learning, Proteomics, Metaproteomics.

Dr. Alex Wan 

Aquaculture, lobster biology, farmed aquatic nutrition, novel aquafeeds, circular aquaculture, aquaculture wastewater   

1) Advancing lobster developmental biology, their dietary requirements and aquaculture. 2) Circular aquaculture feed development, 3) Understanding farmed aquatic animal welfare and husbandry requirements

Background in fish biology and/or lobster biology, applied marine biology

Prof. Mark Johnson 

Marine Ecology 

Improving precision of macroalgal canopy photosynthesis estimates 

Simulation modelling, field work, lab analyses 

Prof. Alan Ryder 

Analytical Chemistry; Bioanalytical sciences; Biophysics; Fluorescence spectroscopy; Raman spectroscopy; Chemometrics/Data analysis.   

1) Excitation Emission Fluorescence Lifetime Matrix (EEFLM) spectroscopy:  The primary aim of this project is to further develop the Excitation Emission Fluorescence Lifetime Matrix (EEFLM) system (hardware, software, theory, applications) which we can use to validate our ARMES-based methods for the rapid quantitative characterisation of proteins.;  2) Using Total Internal Reflection Fluorescence (TIRF) Microscopy to study the interaction of proteins with surfaces.  TIRF localises the fluorescence excitation zone to a nanometre thick (40-200 nm) zone just above the surface of a glass coverslip. This means that only fluorophores in this zone are excited and thus it is an ideal way to study protein-surface interactions; 3) Use of pEEM and single molecule detection (SMD) methods for the quantitative analysis of protein-surfactant interactions.  We have an interest to look at how surfactants interact with proteins.  The idea being that these types are important for understanding the risks involved in packaging therapeutic proteins in polymer vials.  It is also important in the context of understanding problems in the downstream processing of biopharmaceuticals.  

Biophysics, Fluorescence Spectroscopy, Optical Instrumentation. 

Dr. Brian Ward 

Ocean climate; Ocean mixed layer; Air-sea exchange  

Turbulence in the mixed layer and its role in the oceanic uptake of carbon dioxide 

Oceanography background; Matlab/Python coding; field experience; ocean instruments. 

Prof. Louise Allcock 

Evolution and systematics (including molecular systematics) of cephalopods; deep sea, with a focus on octocorals (including sea pens); octocoral research comprising of a mix of phylogenetics / systematics and species distribution modelling with a view to supporting conservation of deep-sea habitats. 

Dr. Enda Barrett 

Cybersecurity, Machine Learning, Artificial Intelligence, Cloud Computing, Energy Management 

Building threat detection and projection models using longitudinal machine learning 

Computer Science, Statistics, Machine Learning, Programming 

Dr. Nazre Batool 

Self-supervised Machine Learning, Foundation Models, Multi-modal Data Processing, Image and Video Processing, Artificial Intelligence, Autonomous Vehicles, Robot Vision

Self-supervised learning techniques for Multi-modal data. The candidate can propose a suitable application with substantial impact.  

Knowledge of self-supervised learning, experience in image and preferably multi-modal data processing, experience in PyTorch and TensorFlow. Some experience in robotics will be a plus.

Prof. Michel Destrade

Nonlinear elasticity; mechanics of soft tissues; electro- and magneto-elasticity; elastic waves 

Anything fitting nonlinear elasticity, mechanics of soft tissues, electro- and magneto-elasticity, elastic waves 

Theoretical mechanics and/or computational mechanics and/or experimental mechanics 

https://scholar.google.com/citations?user=EQAvBLgAAAAJ&hl=en 

Pure Mathematics, Combinatorics, Topology, Graph Theory, Simplicial Complexes, Discrete Geometry (applications and theory), Geometric and Combinatorial Rigidity Theory, Combinatorial Commutative Algebra, Tensor Completability.

Applications of rigidity theory to machine learning and data science. I am particularly interested in exploring connections between recent advances in tensor completability and rigidity of simplicial complexes to applications in AI and machine learning. I will help to design a more detailed proposal around these themes, taking account of the candidate's expertise and interests.

A PhD in a relevant area of mathematics - some expertise in discrete mathematics and/or algebra.

For more information about my research activities see my personal webpage https://www.jamescruickshank.ie/. 

Soil microbial ecology, metagenomics, microbiomes, greenhouse gases, soil carbon, biogeochemistry, antimicrobial resistance in the environment.

Projects focused on microbial processes linked to greenhouse gas emissions, soil carbon, nitrogen cycling, land use change, antimicrobial resistance.

Statistics, Biostatistics, Statistics in Medicine, Sport Science, Medical diagnosis, health monitoring, Medical devices, AI, Machine Learning