Vacancies

The School of Computer Science and Statistics is seeking to appoint an Assistant Professor in Statistics, who will join the Discipline of Statistics and Information Systems. The Assistant Professor will be a full faculty member, and as such will be expected to innovate in both research and teaching, to assist in the administration and mission of the School, and to contribute to the wider research community.

The appointee should have the potential to develop into a leader in their particular field of research with a world-class research programme. The successful candidate will be expected to contribute to undergraduate and/or postgraduate teaching in the School, particularly in areas of Statistics and Information Systems, including contributions to a number of online programmes. 

Context
The successful candidate will be an excellent researcher and lecturer with a strong international research track record and clear potential for research leadership. They should have a strong commitment to research-led undergraduate and postgraduate teaching and a demonstrated capacity/potential to attract research funding and develop a world-class research programme.
Applications are encouraged from candidates in the field of Statistics, who have a genuine commitment to the role of teaching Statistics at undergraduate and postgraduate level across many disciplines in Mathematics, Science, Engineering and the Humanities.

The successful applicant will have a primary degree and a Ph.D. in Statistics or another subject with a strong statistical component. 
The group particularly welcomes applicants who have experience and interest in working in an interdisciplinary setting, and most notably individuals who can exploit the Discipline's position within a School of Computer Science and Statistics

Full Description

 Closing date is Tuesday 7^th April 2026 at noon.

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.

Post Summary

The Research Assistant will work with Dr. Zhang on the collection, curation, processing, and analysis of fMRI and MEG data obtained from open-access neuroimaging repositories. The role will focus on identifying suitable datasets for the study of mental disorders, implementing and comparing different preprocessing pipelines, and applying advanced functional data analysis methods developed within the team to both resting-state and task-based neuroimaging data.

The Research Assistant will also collaborate closely with other team members to test, validate, document, and improve the usability of the team’s Python-based functional data analysis tools, and to support the dissemination of research findings through reports, presentations, and academic publications.

Hours of Work: 8 hours per week  

Closing Date: 12 Noon (GMT), 20 April 2026 

Full Details

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

The role
This postdoctoral position contributes to a UKRI-funded programme developing anatomy-driven artificial intelligence for translational neuroscience, with a focus on understanding how cognitive computations emerge from cortex-wide neural dynamics across species.

The PDRA will contribute primarily to developing and analysing Cortically-Embedded Recurrent Neural Networks (CERNNs) that simulate large-scale neural dynamics during cognitive tasks. These models integrate species-specific neuroanatomical constraints to enable principled comparisons between human, macaque, marmoset, and rodent cognition.

The postholder will be physically based at Trinity College Dublin, embedded in the School of Computer Science and Statistics, Artificial Intelligence Discipline, and the Trinity College Institute of Neuroscience (TCIN), where they will interact closely with experimental and computational neuroscience groups. They will hold visiting researcher status at Trinity while being employed and line-managed by the University of Oxford.

This role is well suited to a postdoctoral researcher seeking advanced training at the interface of computational neuroscience, neuroanatomy, and AI, with opportunities to build expertise towards future fellowships or faculty positions.

Closing date is 1^st April at 12:00 midday

Full Description & Application Details