Vacancies

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'.

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Project title: Developing and Evaluating Interpretable Approaches for Human-Centered Machine Learning.

Project supervisor: Dr. Eoin Delaney (Trinity College Dublin).

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

Application deadline: 12th May 2025

Start date: Anticipated start date is September 2025

PhD structure: The funding for the project includes a tax-free stipend of €25,000 per annum. In addition to stipend, EU fees will be covered for four years.

PhD topic: Approaches in interpretable machine learning offer promise in understanding the predictions of opaque models that are widely deployed in high-stakes decision making scenarios. Of particular interest to this project are example-based
explanation methods that use individual data points or examples to provide insights into the decision making process of complicated models. This family of explanations has both rich statistical and psychological grounding and the project will look to investigate the utility and the robustness of different explanation strategies.

A large focus of this project will be on leveraging novel and interpretable approaches in applied domains such as algorithmic fairness and time series prediction for sustainable practices. As explanations are ultimately for end users, it is crucial to consider how they can impact trust in automated decision-making, creating a need for human-centred and psychologically informed methods that could encourage responsible behaviours. Designing such approaches for opaque generative models, such as Large Language Models (LLMs), is critical and is also of growing interest to industry. 

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Conditions of the Award

The conditions are listed on the TCD website: https://www.tcd.ie/graduatestudies/awards/trda-school-based/

• Open only to new entrants to the full-time research doctorate register (EU and non EU).
• The holders must engage in full-time research and must register for a research doctorate degree at Trinity College, the University of Dublin.
• Continuation on the research register is dependent on evidence of satisfactory annual progress and successful completion of the confirmation process at 18 months after first registration.
• Both the doctorate researcher and supervisor will agree to participate in the pilot rollout of Trinity’s Supervisor: Research Student Agreement.
• Postgraduate Research Doctorate Awards cannot continue beyond a fourth year on the full-time research doctorate register and cannot be split across doctorate researchers.
• Decision on allocation of the award rests with the School.

Stipend details

The award includes a stipend of €25,000 p.a. and fees write-down  (EU or non-EU) for the four years (full-time) of a Structured PhD programme / research doctorate.

Application criteria

The award is expected to be very competitive, therefore the minimum requirement is a 2.1 honors (or equivalent) undergraduate degree and a distinction (or equivalent) MSc degree. Candidates without an MSc or a lower grade degree will not be considered. 

Application process

All application materials should be submitted by the prospective supervisor, no later than Friday May 30th, with the subject line “School-based Trinity Doctoral Research Award” to SCSS DTLP at gavin.doherty@tcd.ie, HoS at headscss@tcd.ie and Natasha Blanchfield at Natasha.Blanchfield@tcd.ie. 

Late or incomplete applications will not be considered.

Applications should contain the following in pdf format:

Provided by the candidate:

2.    CV

4.    Proof of English language qualifications, if required (according to English language requirements on https://www.tcd.ie/study/apply/admission-requirements/postgraduate/index.php)

5.    2 academic reference letters

6.    2 page research proposal, to also address how does the proposed research fit within SCSS research themes and proposed supervisor’s interests

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.

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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. 

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Project title: AI-Driven Energy Optimization in Next-Generation RANs
Project supervisor: Dr. Merim Dzaferagic (Trinity College Dublin).
Project locations: Discipline of Networks and Distributed Systems, School of Computer Science and Statistics, Trinity College Dublin.
Application deadline: 10th May 2025
Start date: 1st September 2025.

PhD structure: This is a full-time 4-year structured PhD project, based in the Discipline of Networks and Distributed 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: The disaggregation of Radio Access Networks (RANs) introduces new challenges in energy management. Unlike traditional architectures with static power allocation, disaggregated RANs allow for dynamic scaling and flexible placement of
network functions, which significantly impacts power consumption. Initial research has shown that the placement of network functions, along with their scaling up and down, plays a critical role in overall energy efficiency. However, there is a significant
gap in real-world experimentation and data collection, particularly in an end-to-end network deployment that integrates AI-driven energy optimization strategies.

Key challenges include collecting relevant energy consumption data, understanding its correlation with network performance, and developing AI-driven power management strategies that dynamically adapt to traffic demands. The project will involve hands-on experimentation with real networking equipment in the OpenIreland testbed, enabling the validation of AI-based techniques in a live disaggregated network environment. By evaluating the impact of function placement and scaling on power
efficiency, the research will quantify trade-offs between energy savings and network performance under different AI-driven approaches.
 
The expected outcomes include a better understanding of how disaggregated network architectures influence power consumption, along with the development of AI driven energy optimization techniques tailored for real-world deployments. By integrating experimental insights from OpenIreland, this project will bridge the gap between theoretical energy models and practical network operation. 

Full Job Description

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

Project title: Modeling Large-Scale Digital Trace Data with Local Dependence

Project supervisor: Dr. Cornelius Fritz (Assistant Professor at Trinity College Dublin).

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

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: Surrounded by smart devices that collect interpersonal data,we must explore novel ways of measuring and understanding social behavior through digital trace data, such as email traffic, follower networks on a social platform, or spatial co-location networks. The gathered data offers a planetary-scale view of online interpersonal relations, enabling a more nuanced investigation of biases in information diffusion, polarization, and echo chamber effects. To harness
this information, novel models that handle large network sizes and additional information – such as fine-grained temporal information for email traffic or additional neighborhood structures – are essential. A key challenge in modeling large networks is ensuring local dependence in the assumed model, reflecting the natural perception that individuals primarily interact within their local neighbourhoods rather than the entire network.

To address this, the PhD candidate will develop network models for such trace data with a strong focus on real-world applications. These applications can be developed in collaborations with substantive scientists from, e.g., Sociology, Political Science, or Economics. The PhD candidate will also develop state-of-the art optimization algorithms specifically tailored to the proposed large-scale network models. These methods will be implemented in efficient, scalable software packages, ensuring their applicability to real-world social and computational challenges.

Application deadline: 30th April 2025

Start date: 1st September 2025

Full Details

Project title: Explaining and Mitigating Bias in Advice from Large Language Models.

Project supervisor: Dr. Eoin Delaney (Trinity College Dublin) and Prof. Mark Keane (University College Dublin).

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

Application deadline: 02 June 2025. Applications will be reviewed on a rolling-basis and early application is strongly recommended.

Start date: September 2025.

PhD structure: The funding for the project includes a tax-free stipend of €25,000 per annum. In addition to stipend, fees will be covered for four years and there is a budget for equipment and conference travel. This project is supported by the Central Bank of
Ireland and the Insight Centre for Data Analytics which is funded by Research Ireland.

PhD topic: This project aims to address how language models can nudge people towards irresponsible behavior by amplifying existing cognitive biases. We aim to: (1) develop novel human-centered explanation techniques that help people to critically
evaluate generative AI models with a focus on financial advice; (2) provide empirical evidence on how advice from Large Language Models (LLMs) can be overconfident and biased; and (3) create practical guidelines for responsible AI system design. By
combining insights from cognitive psychology with explainable AI (XAI) techniques, our work directly supports the Central Bank's regulatory objectives under the EU AI Act by addressing systems that could deploy subliminal, manipulative, or deceptive techniques, impairing informed financial decision-making. The resulting frameworks from this project will equip practitioners with evidence-based approaches for evaluating and regulating AI advisory systems while protecting people as these technologies become increasingly prevalent. 

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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

 Post Summary
We are seeking to recruit a post-doctoral research fellow to investigate the use of deep reinforcement learning (RL) and swarm intelligence techniques to optimize urban and highway traffic in the presence of autonomous and conventional vehicles within the ClearWay project funded by Science Foundation Ireland.

The increasing availability of sensor data, including from connected and autonomous vehicles, will make it possible to capture the detailed state of a road network in real time. Along with the ability to exercise an increasing level of control over individual vehicles either indirectly, e.g., via urban traffic control or driver guidance systems, or directly, in the case of (semi-) autonomous vehicles, this offers the opportunity to deploy new approaches to traffic management with the explicit goal of optimizing end-to-end travel-time reliability.

Extending the state of the art in deep RL and swarm intelligence, ClearWay will design algorithms for optimization of urban and highway traffic as a function of the increasing levels of sensor data and increasing levels of control over individual vehicles available. Such algorithms will need to take account of the scale, complexity, and inherent non-stationarity of traffic systems, while adapting to the many transient perturbations that effect traffic flow. The focus is on decentralized and multi-agent algorithms that allow traffic controllers to cooperate towards system-wide optimal solutions. The successful candidate is expected to make contributions to the state of the art in deep RL applied to cyber-physical systems in areas such as multi-agent cooperation, lifelong learning, transfer learning, and explainability.

The position will be based in the School of Computer Science and Statistics at Trinity College Dublin, Ireland under the direction of Prof. Vinny Cahill and Prof. Ivana Dusparic.

Full Role Description

We are seeking a highly motivated candidate for a research assistant 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 research assistant is expected to assist the team with fundamental research and engineering problems, supporting publications 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 implementations and engagement in other research activities such as reading group, workshop organization, etc. 

The start date for the position is flexible, with a preferred start in early 2025. Contract
duration is 1 year with the option of renewing for a second year.

Full Job Description

Post Summary

This position offers an exciting opportunity to join the Complex Software Lab in the School of Computer Science and Statistics at Trinity College Dublin.

The successful candidate will work on a project examining rugby dynamics by means of video annotation and coding for automated tackling analysis. The role involves careful review and systematic labelling of large volumes of rugby footage to support Machine Learning applications and performance analysis. This hands-on role is particularly suited to applicants with a background or strong interest in sports video analysis, biomechanics, or performance studies.

Standard Duties and Responsibilities of the Post

· Systematic coding, labelling, and annotation of rugby video footage, focusing on tackles.

· Maintenance of accurate databases, video logs, and annotations.

· Collaboration with a multidisciplinary research team (including data scientists, sports scientists) to develop robust video datasets suitable for automated analysis.

· Adherence to data protection and ethical guidelines in handling video content.

· Participation in project meetings, presenting updates on labelling progress and any observations from the data.

· Contributing to reports, presentations, and where appropriate, academic publications arising from the research.

Closing Date: 12 Noon (GMT), April 16th 2025

Full Job Description

Post Summary
We are seeking to recruit a Postdoctoral Research Fellow to investigate the use of machine learning for sustainable mobility, within CONNECT - the Research Ireland Centre for Future Networks and Communications in collaboration with the E3 Kinsella SUMMIT (SUstainable MobIlity Models for a Just Transition) initiative.
This project will seek to extend the state-of-the art in machine learning to support the delivery of personalized mobility services at scale, in particular, enabling collaborative journey planning in the context of limited transportation capacity.

Standard Duties and Responsibilities of the Post
The successful candidate is expected to make contributions to the state of the art in machine learning applied to cyber-physical systems in areas such as deep/reinforcement learning, multi-agent cooperation, continual learning, and/or transfer learning. The position will be based in the CONNECT research centre at Trinity College Dublin, Ireland. The researcher will also be part of the School of Computer Science and Statistics in TCD. The position will be under the direction of Dr. Mélanie Bouroche. 

Funding Information
The post is funded by Research Ireland, as part of CONNECT the SFI research centre for Future Networks and Communications.

Full Role Description

The ClearWay1 project at TCD is exploring ‘slot-based driving’ (SBD) as a strategy for active management of roads. SBD abstracts traffic as flows of slots with each slot having a specified trajectory. Vehicles are associated to slots and their trajectories are intended to follow those of their assigned slots. In practice, depending on the level of control available and/or the degree of compliance of drivers with guidance, vehicles may depart from their slots. However, slots provide an abstraction over which the road traffic management system can reason and exercise control. In particular, we are exploring the use of reinforcement learning to have controllers learn appropriate slot-management strategies.

Internship project: Model-based RL for Autonomous Driving
This project will investigate applicability of model-based RL (MBRL)2 in transportation and 
autonomous driving. 

Skills required:
Excellent computer programming skills in Python as well as previous experience of autonomous driving simulators and reinforcement learning. 

Application details:
The positions are full-time and tenable immediately for up to twelve weeks (expected to finish no later than the 31st of August 2025). As full-time positions, applications from continuing students enrolled in full-time courses who have coursework to complete over the summer months (e.g., a dissertation) are not eligible.

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