PhD Opportunity – Artificial Intelligence for Athlete Monitoring and Performance Optimisation

The Department of Computer Science and Information Systems (CSIS) at the University of Limerick (UL) invites applications for a fully funded PhD position on Artificial Intelligence for Athlete Monitoring and Performance Optimisation.This interdisciplinary project is a collaboration between CSIS, the Department of Physical Education and Sport Sciences (PESS), and Munster Rugby. The research will bridge cutting-edge AI and machine learning with applied sports performance, developing intelligent systems that enhance athlete health, readiness, and wellbeing through multimodal data analysis and predictive modelling.The successful candidate will contribute to the design, development, and validation of scalable, data-driven frameworks capable of integrating diverse athlete datasets (e.g., GPS, biomechanics, physiology, and wellness). A focus on explainable and ethical AI will ensure that outputs are interpretable, trustworthy, and relevant in real-world, high-performance environments.This position offers unique opportunities for:Hands-on engagement with elite athlete data and high-performance practitionersCollaboration with sport scientists, coaches, and data analystsImmersion in a research environment combining technical innovation and applied impact Eligibility CriteriaApplicants should demonstrate strong technical, analytical, and research capabilities suitable for advanced PhD-level work in AI and data-driven sport science.Essential Technical RequirementsA Master’s degree (or equivalent) in Computer Science, Data Analytics, Artificial Intelligence, or a closely related field.·        Strong background in machine learning and deep learning (e.g., CNNs, RNNs, Transformers).·        Experience with multimodal data processing and predictive analytics.·        Proficiency in Python and AI/ML libraries (PyTorch, TensorFlow, Scikit-learn).·        Knowledge of data pipelines, preprocessing, and database management.·        Understanding of sports science or human performance concepts, including athlete monitoring and performance metrics.·        Awareness of ethical AI practices, data privacy, and FAIR data principles.Excellent communication skills in English (IELTS ≥ 6.5 or equivalent, if applicable).   Applied and Contextual Understanding (Highly Desirable)·        Understanding of sports science and athlete monitoring systems, ideally in a rugby or team sport context.·        Appreciation of applied high-performance environments and interdisciplinary collaboration.·        Familiarity with biomechanical, physiological, psychological, nutritional, or wearable device data.·        Interest in bridging AI research and sport performance practice through interpretable, real-world systems.Additional Desired SkillsKnowledge of signal processing, time-series, or multimodal data analysis.Experience with transfer learning, explainable AI (XAI), or cross-modal data integration.Familiarity with cloud computing platforms (AWS, GCP, Azure) and scalable deployment.Strong communication and teamwork skills, with the ability to collaborate across disciplines.Motivation, initiative, and commitment to high-quality research outputs.A genuine passion for sport and data-driven innovation in athlete performance and wellbeing.Research TeamLead SupervisorDr. Fazilat Hojaji, Associate Professor, Department of Computer Science & Information Systems Joint Supervisor(s)Prof. Mark Campbell, Professor, Department of Physical Education & Sport SciencesDr. Catherine Norton, Associate Professor, Department of Physical Education & Sport SciencesIndustry Partner Dr. Georgia Scott, Sport Scientist at Munster Rugby Application ProcessInformal inquiries are welcome prior to submission.To apply, please submit one single pdf including :A detailed CV (max 3 pages)Academic transcripts (undergraduate and postgraduate)A 1-page statement of research interest outlining your motivation, skills, and fit for this projectTwo reference lettersApplications to: Fazilat.Hojaji@ul.ie       Deadline: 10 December 2025