Vacancies

AI Engineering emerges from a fundamental shift in how we build software systems. Modern applications increasingly embed AI/ML components that generate substantial business value and/or ensure the correct operation of critical systems: Recommendation engines driving e-commerce revenue, LLMs powering intelligent interfaces, computer vision enabling medical diagnosis, predictive models informing financial decisions, and autonomous systems used in safety-critical environments like transportation and healthcare.
Unlike traditional code, where behaviour is explicitly specified through programmed logic, these AI components learn patterns from data, making their behaviour emergent rather than programmed. This fundamental difference introduces new engineering challenges around uncertainty, interpretability, and robustness. The complexity intensifies when multiple models are combined, or multiple AI agents collaborate to achieve intended system objectives, a scenario increasingly common in real-world deployments.

Some examples of potential research areas include:
• Software Engineering for AI-enabled systems: Adapting traditional techniques to handle the inherently different nature of AI/ML components—including requirements engineering for uncertainty, AI-aware software design, testing probabilistic systems and agentic AI systems, and ensuring reproducibility across evolving pipelines.
• Particular challenges arise when engineering approaches need to consider recent developments in AI, which are also under heavy demand in current industry practice, e.g., model refresh, continual learning, trustworthiness of AI, agentic AI, and agents pursuing long-term objectives.
• Optimisation and Metaheuristics: Multi-model, multi-agent, and whole-pipeline optimization, balancing accuracy with computational costs, and systematically exploring high-dimensional configuration spaces to discover non-obvious solutions.
• Model and System Evolution: Detecting and managing data and model drift, triggering appropriate retraining, and developing specialized CI/CD pipelines with statistical validation and rollout/rollback strategies.
• Systems Engineering: Taking a whole-systems perspective that considers interactions across data collection, training, inference, and monitoring—including multi-agent coordination, conflict resolution, and system-level guarantees despite individual agent uncertainty.

Full Details

Deadline: Rolling basis, with a deadline of 31 January 2026 for applicants who wish to begin before September 2026.

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 29th May, 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:

1.    Cover letter

2.    CV

3.    Undergraduate and postgraduate transcripts

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

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