진학프로석·박사 채용 접수 플랫폼

Position Overview

The Intelligent Efficiency Research Group (IERG) which is part of the Sustainability Institute at University College Cork is seeking a highly motivated PhD candidate to undertake a fully funded PhD studentship focused on AI-enabled fault detection, diagnostics (FDD), and predictive maintenance for industrial systems. The position forms part of the EU-funded FLARE project, which addresses digitalisation, flexibility, resilience, and sustainability in industrial operations.

The PhD will develop and implement artificial intelligence and data-driven methods for early anomaly detection, root cause diagnosis, and failure prediction on industrial systems, leveraging digital twins and advanced analytics. The work will support equipment-level deployment of predictive maintenance services while ensuring robustness, explainability, and alignment with industrial reliability and operational excellence practices. Three industry partners will be utilised as test beds for the research.

The student stipend is €25,000 per annum with additional travel and research costs provided, including a laptop.

Key Objectives

• • Develop and implement AI-assisted fault detection and diagnostics methods for industrial equipment and processes, including data-driven and hybrid (physics-informed) approaches.
• • Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions.
• • Integrate AI models with digital twins and industrial data streams to enable real-time anomaly detection and condition monitoring.
• • Apply and extend reliability and performance metrics (OEE, MTTF, MTTR) to quantify equipment health, availability, and resilience under flexible operating conditions.
• • Combine AI-based insights with LEAN and Six Sigma methodologies to support structured root cause analysis and continuous improvement.
• • Provide validated outputs to predictive control frameworks, operator decision support systems, and sustainability assessment work packages within FLARE.
• • Benchmark and validate AI-enabled methods using real industrial case studies and project-wide evaluation activities.

Supervisory Team

• • Lead Supervisor: Dr Ken Bruton, Intelligent Efficiency Resource Group (IERG), UCC
• • Co-Supervisor: Dr Dominic O’Sullivan, Intelligent Efficiency Resource Group (IERG), UCC

Criteria

• • A minimum qualification of a 2.1 honours BSc or MSc in a scientific discipline (i.e., engineering, computer science, artificial intelligence, or similar) is essential for the PhD focused on ‘Technology’.
• • Candidates should be highly self-motivated, able to demonstrate initiative and have a desire to learn, and able to work as part of a multidisciplinary research team.
• • The successful candidate will be highly analytical and motivated with good interpersonal and organisational skills and will be self-managed and achievement oriented.
• • Strong written and verbal communication skills in English suitable for technical documentation, presentations, and publication.
• • A minimum level of competency in English is required for registration at UCC. Please see the following link regarding English Language requirements: (https://www.ucc.ie/en/study/comparison/english/).

Desirable experience includes:

• • Fault detection, condition monitoring, or reliability engineering
• • Machine learning or AI methods (e.g. anomaly detection, classification, regression, time-series modelling)
• • Programming skills (e.g. Python, MATLAB or similar)
• • Experience with industrial systems, automation, or control
• • Experience of publishing, and/or disseminating research findings.
• • A strong interest in digitalisation, resilience, and sustainable industrial operations

Key Responsibilities:

Key Responsibilities of the successful PhD candidates will include:

• • Performing research with responsibility and integrity.
• • Providing weekly updates to your supervisor/supervisory team.
• • Present research findings at key international conferences and publish in high-impact journals.
• • Complete required PhD coursework, participate in group meetings, required placements, and contribute to outreach activities.
• • Participate in Education and Public Engagement activities, as required.
• • To carry out any additional duties that may reasonably be required within the general scope and level of the post.

How to Apply

Applicants should submit the following documents in a single PDF:

1. 1. Curriculum Vitae (CV)
2. 2. Cover letter (maximum 1 page) outlining research interests and suitability for the project
3. 3. Academic transcripts (undergraduate and postgraduate, where applicable)
4. 4. Contact details for two academic referees

Applications should be sent by email to:
ken.bruton@ucc.ie with subject line: “PhD AI-Enabled Predictive Maintenance – Applicant Name”

Closing date for applications: 27th March 2026

Interviews are expected to take place in May/June 2026.

It is intended that the successful candidate will start their posts on the 1st of October 2026 or before if feasible