Luiza Piancastelli
3 months ago
Fully Funded PhD in Bayesian Ranking Models, Statistics, and AI at University College Dublin University College Dublin in Ireland
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableDeadline
December 31, 2026Country
Ireland
University
University College Dublin

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About this position
A fully funded PhD position is available at University College Dublin under the supervision of Dr Luiza Piancastelli, Assistant Professor and Ad Astra Fellow in Foundational Statistics for AI. The project focuses on developing advanced Bayesian probabilistic models for ranking data, which are prevalent in applications such as recommender systems, marketing research, social choice, and bioinformatics. The research will emphasize two-way clustering, high-dimensional feature selection, and modeling complex interdependencies, with a strong focus on Bayesian inference and computational methods.
The successful candidate will build on the Bayesian Mallows model and the Clustered Mallows Model, aiming to advance frameworks for clustering rankers and items, incorporating high-dimensional covariate information, and developing new Bayesian inference procedures. The project is computationally intensive and ideal for students interested in statistics, machine learning, and AI.
Applicants should have a solid background in statistics or computer science, with a strong interest in probabilistic modeling and computational methods. Prior experience with Bayesian inference, statistical computing, or machine learning is advantageous. The position offers a €25,000 tax-free stipend, €4,000 annual travel budget, and full tuition coverage. The start date is September 2026. For more details, see the attached project description and apply via the provided application form link.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
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