Trinity College Dublin is offering a fully funded 4-year PhD position in the School of Computer Science and Statistics, focusing on the intersection of statistics, generative artificial intelligence (GenAI), and higher education. The project, supervised by Dr. Emma Howard, aims to rigorously evaluate the impact of GenAI tool use on student learning and assessment in higher education. The research will involve designing and implementing both observational and experimental studies, collecting and analyzing data, and developing new statistical models to interpret the results. The project will address challenges such as observational data, short-term measurement, and uncertain GenAI usage, with a strong emphasis on open science and reproducibility.

The successful candidate will join a top-ranked research-intensive institution, with the School of Computer Science and Statistics recognized for academic excellence and ranked 1st in Ireland. Applicants should have or expect to attain at least a 2.1 honours degree (or equivalent) in statistics, applied mathematics, education, psychology, or a related field. Experience with statistical computing (preferably R) and an interest in STEM or higher education research are essential. Proficiency in statistical analysis, machine learning, or statistical modelling is required for those from education/psychology backgrounds. English language proficiency is required for non-native speakers. A Master's degree and relevant research or practical experience are desirable.

The position is fully funded, offering a tax-free stipend of €25,000 per annum and covering EU tuition fees for four years. The anticipated start date is 1st September 2026, with the application deadline on 8th March 2026. Applicants should submit a single PDF document including a cover letter, CV, and academic transcripts to Dr. Emma Howard ([email protected]), using '[PhD Stat & HE] Your name' in the subject line. Informal queries are welcome.

Keywords: statistics, generative artificial intelligence, higher education, student learning, assessment, statistical computing, educational research, machine learning.