Up to 30% off — ends 2 Aug
ONLY00h00m00s
Up to 30% off — ends 2 Aug
ONLY00h00m00s
Queen's University
4 days ago
Fully Funded PhD and MSc Opportunity in Natural Language Processing and Large Language Models at Queen’s University Queen’s University in Canada
Degree Level
Master's, PhD
Field of study
Computer Science
Funding
Full funding availableCountry
Canada
University
Queen's University

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About this position
Fully funded PhD and MSc opportunities are available at Queen’s University in Canada for students interested in Natural Language Processing (NLP), Large Language Models (LLMs), Multimodal AI, Retrieval-Augmented Generation (RAG), Trustworthy AI, Explainable AI, Machine Learning, Deep Learning, Knowledge Graphs, and Information Retrieval.
The research aims to develop novel AI methods that improve the capability, efficiency, reliability, and safety of language models while addressing practical challenges across real-world application domains.
Applicants from Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, Statistics, or related areas are encouraged to apply.
Preferred qualifications include a Bachelor’s or Master’s degree in a relevant discipline, strong Python programming skills, and experience with machine learning, deep learning, or NLP. Strong analytical, research, and communication skills are also expected.
Funding includes a competitive stipend, tuition support, publication opportunities, and access to high-performance computing resources.
To apply, email your CV, academic transcripts, and a brief statement of research interests to Dr. Ruifeng Xu. An official faculty profile is linked in the post for additional context.
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.
More information can be found here
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