Vacancies

Welcome to the School of Computer Science and Statistics! We are a thriving multidisciplinary school encompassing five disciplines with over 130 academic, teaching, research and support staff. The school hosts two cutting-edge SFI Research Centres and is located across eight locations on campus. As an integral part of the E3 initiative, we collaborate closely with the Schools of Engineering and Natural Sciences to drive ground-breaking research and education.

Trinity Campus

Our commitment to excellence is evidenced by being the leading university in Ireland in Computer Science and ranked in the Top 100 globally.

Whether you are starting your academic career or seeking to advance your expertise, the School of Computer Science and Statistics is the perfect place to thrive and innovate.

PhD Opportunities

A number of fully funded PhD scholarships are available  in the area of Predictive Maintenance & Early Warning Systems. The Studentships will provide a tax-free bursary of €25,000 per annum for 4 years together with fee payment. Applications ought to have a 2.1 or 1 Degree in Computer Science or cognate discipline and/or an M.Sc in Computer Science/cognate discipline. Applications will be received until the positions are filled.

 Prospective candidates would be expected to have an interest in several of the following areas: Artificial Intelligence, Multi-Agent Systems, Machine Learning, Data Analytics, IoT systems & Ubiquitous Sensing. Interested parties should send a detailed academic Curriculum Vitae together with a letter of application indicating their interest in research, Trinity College SCSS and CKDelta programme, to Professor Gregory O’Hare Gregory.OHare@tcd.ie with a subject heading 'AI PhD Application'.

Full Details

Project title: T-DIET - Developing novel statistical methods for the analysis of longitudinal dietary patterns and their association with health outcomes

Project supervisor: Dr. Silvia D’Angelo

Project locations: Discipline of Statistics and Information Systems, School of Computer Science and Statistics, Trinity College Dublin.

Application deadline: 30th April 2025
Start date: 1st September 2025

PhD structure: This is a full-time 4-year structured PhD project, based in the Discipline
of Statistics and Information Systems at Trinity College Dublin. The funding
for the project includes a tax-free stipend along with expenses for computing equipment,
conference travel and materials. Fees are provided for in the funding.

The T-DIET project will develop novel statistical methodology to enable inference on dietary patterns, i.e., groups capturing different diets in a population, from longitudinal food intake data. The framework will rely on an Hidden Markov model (HMM), a type of latent variable model allowing to infer unobserved groups underlying longitudinal data, the dietary patterns.

Further, it will allow one to model, in probabilistic terms, individuals’ adherences to such patterns, permitting changes of diets over time, and directly quantifying uncertainty. Various complexities will be addressed, such as the compositional nature of intake data, or the incorporation of prior information available in the Nutrition literature on dietary patterns, e.g. their qualitative ordering.

Full Details

 

Project title: Developing novel statistical methods for the analysis of complex multidimensional
networks

Project supervisor: Dr. Silvia D’Angelo (Trinity College Dublin).

Project locations: Discipline of Statistics and Information Systems, School of Computer Science and Statistics, Trinity College Dublin.

Application deadline: 30th April 2025
Start date: 1st September 2025.


PhD structure: This is a full-time 4-year structured PhD project, based in the Discipline of Statistics and Information Systems at Trinity College Dublin. The funding for the project includes a tax-free stipend. EU fees are provided for in the funding. 

PhD topic: Properties of network data have been explored in depth; however, there is lack of methodology for the analysis of many specific network data-types. A particular type of network data are multidimensional networks, which correspond either to relations evolving over time or to different relation types recorded between subjects.
The research goal is to work with complex networks and multidimensional networks, developing novel methodology tailored for the analysis of such data, with particular focus on modeling the dependence between multiple networks using a latent variable construct and Bayesian inference. The purpose is to provide information on the interdependence between different types of relations among a group of subjects. 

Full Details

 

 

Overview: Smart contracts are transforming how we can enhance automated public services and how 
the services are executed and controlled in digital ecosystems. Distributed artificial intelligence (AI) is a new AI paradigm that uses distributed device computing resources to improve data privacy. The 
combination of smart contracts and distributed AI has the potential to address challenging research 
issues, including data privacy, security, and network scalability, thus fostering efficient and responsible actions for reliable digital transformation ecosystems. Although promising, there is no high-level 
reference framework for designing novel smart contract driven distributed AI solutions and a lack of
benchmarking testbeds for essential use cases and scaling their implementation internationally. 

Focus and role: The successful applicant for the position will investigate innovative approaches using smart contracts and distributed learning to improve public digital services in smart cities and 6G. The
research will primarily focus on developing 1) reliable distributed AI models for optimising resource 
allocation and misbehaviour detection in connected applications, 2) decentralised AI solutions to 
improve security protection and privacy preservation of interconnected smart cities, and 3) personalised learning solutions for hyperconnected digitalization systems (including 6G). Of particular interest is the demonstration of the developed solutions in O-RAN and Hyperledger-based 6G networks.

Position: The position is fully funded for 3 years. The successful applicant will receive: 
• an annual stipend of €22,000 (tax-free, for 3 years), and
• an annual PhD fee of €5,500 fee to pursue a PhD in Computer Science.

Full Description

Closing Date: 28th February 2025

 

Research Opportunities

We are seeking a highly motivated candidate for a fully funded postdoctoral researcher
position to work in 3D computer graphics and 3D computer vision.


The successful candidate will join the 3D Graphics and Vision research group led by
Prof. Binh-Son Hua at the School of Computer Science and Statistics, Trinity College
Dublin, Ireland to work on topics related to generative AI in the 3D domain.


The School of Computer Science and Statistics at Trinity College Dublin is a collegiate,
friendly, and research-intensive centre for academic study and research excellence. The
School has been ranked #1 in Ireland, top 25 in Europe, and top 100 Worldwide (QS
Subject Rankings 2018, 2019, 2020, 2021).


The postdoctoral researcher is expected to conduct fundamental research and publish
in top-tier computer vision and computer graphics conferences (CVPR, ECCV, ICCV,
SIGGRAPH) and journals (TPAMI, IJCV). Other responsibilities include supporting
graduate or undergraduate students with technical guidance and engagement in other
research activities such as paper reviews, reading group, workshop organization, etc.


The start date of the position will be as soon as possible. Contract duration is 1 year with
the option of renewing for a second year.


The successful candidate will require the following skills and knowledge:
• PhD in Computer Science or related fields;
• Strong tracked records in 3D computer graphics, 3D computer vision;
• Hands-on experience in training deep models and generative models is required;
• Hands-on experience and relevant skills in computer graphics and computer
vision application development such as OpenGL, OpenCV, CUDA, Blender is
desirable;
• Strong programming skills in C++, Python. Capability in implementing systems
from research papers and open-source software.
• Additional background in math, statistics, or physics is an advantage.

Full Description: Postdoctoral Researcher