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.
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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'.
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.
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.
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.
Closing Date: 28th February 2025
Research Opportunities
Post Summary
A postdoctoral researcher position is available with the Graphics and Vision Discipline in the School of Computer Science and Statistics (SCSS), Trinity College Dublin (TCD) to work on a research project funded by Horizon Europe, with the goal of creating novel human-centric tools for content production and consumption via social virtual reality.
Duration of post: To start as soon as possible and until end of project, September 30th 2025.
Standard Duties and Responsibilities of the Post
The postdoctoral researcher will work closely in collaboration with colleagues in TCD and a multi-institutional project consortium, towards the completion of several Work Packages of an EU-funded collaborative research project. Some key areas of responsibility are as follows:
XR CREATION: Contribute to development of tools that will enable story creators to produce novel XR media experiences. Contribute to the evaluation of media description formats, hardware and software components to facilitate XR content creation, focussing on 3D capture, from multiple sources, of storytellers, journalists, or performers, but also supporting 2D video, audio, and text.
XR AUDIENCE EXPERIENCES: leverage a range of technologies to enable active immersive content consumption experiences for end users. Contribute to the evaluation and definition of such technologies.
VOLUMETRIC VIDEO and HUMAN MOTION MODELS: contribute research and development efforts in Computer Graphics, AI and Computer Vision for analysis and processing of volumetric video and human motion data, to allow better usage and integration of such content into XR experiences, enhance the properties of volumetric data and enable better integration with established computer graphics and animation pipelines.
DEPLOYMENT OF PILOTS: collaborate with use case partners in the development and evaluation of pilot demonstrators
COMMUNICATION AND DISSEMINATION: attend recurrent meetings with inter-institutional partners to plan and advance project goals, contribute to presentation and written reporting of work completed, and participate in publishing results and increasing the visibility of the project among targeted communities and the public. The researcher will be expected to travel abroad (within Europe) for some project meetings.
Closing Date: 12Noon March 18th 2025, or as soon as the post is filled thereafter.
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