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PhD & MSc Positions in NLP and Large Language Models at Queen’s University

Queen’s University Text Analytics and Machine Learning Group (TAML)
✓ Fully Funded ⏰ Closing Soon 🎓 Artificial Intelligence 🎓 Computer Science 🎓 Electrical Engineering machine learning natural language processing large language models agentic models text analytics computational linguistics ai research vector institute

Explore research in Large Language Models and NLP to develop AI that understands and interacts through human language. Work within a leading AI group at Queen’s University to contribute to transformative technology impacting multiple industries.

AI-generated overview

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Why This Research Matters

This research enhances AI systems' natural language understanding, crucial for intelligent virtual assistants, automated language translation, and more. By improving how machines process human language, it aims to foster better human-computer interaction and enable new AI capabilities with broad societal benefits.

Natural language processing machine learning artificial intelligence

Project Description

Project Overview

This research focuses on advancing Large Language Models (LLMs), Natural Language Processing (NLP), and agentic models, crucial for the next generation of AI applications like intelligent assistants, automated translation, and advanced information retrieval. The goal is to enable AI systems that understand, generate, and interact with human language effectively.

What You Will Do

As part of the Text Analytics and Machine Learning Group (TAML) led by Dr. Xiaodan Zhu, students will engage in research involving cutting-edge machine learning techniques, work with large-scale language models, and explore agentic models. You will have access to world-class computing infrastructure, including advanced GPU resources at the Vector Institute. Collaborative research with academic and industry partners is expected.

Expected Outcomes

Outcomes include novel methodologies for enhancing LLM and NLP capabilities, improved machine intelligence for better human-computer interaction, and contributions to the scientific understanding of agentic AI models. Graduates will be well-equipped for careers in academia and industry.

Why This Matters

AI systems capable of sophisticated natural language understanding support transformative applications across healthcare, education, finance, and entertainment. Developing these systems addresses fundamental machine intelligence challenges and can significantly improve societal interactions with technology.

Entry Requirements

Strong academic background in computer science, electrical engineering, artificial intelligence, data science, or computational linguistics. Demonstrated research potential and interest or experience in NLP, LLMs, agentic models, or machine learning. Proficiency in programming and relevant AI/ML frameworks is expected.

How to Apply

Apply by filling out the Google form at https://lnkd.in/e6Y7YnFq and refer to the official LinkedIn post for further details: https://www.linkedin.com/posts/xiaodan-zhu-066833101_programs-and-departments-share-7437319024742645760-ntE1

Eligibility

UK/Home
EU
International

Supervisor Profile

DX
Dr. Xiaodan Zhu
Queen’s University, Text Analytics and Machine Learning Group (TAML)

Dr. Xiaodan Zhu is a leader in natural language processing and machine learning, with a faculty position at Queen’s University and a dual appointment at the Vector Institute for AI. Her research focuses on advancing AI's ability to understand and generate language, particularly through large language models and agentic AI approaches. Dr. Zhu has a strong reputation in the AI community for innovative research contributions.

Key Publications

2017 1590 citations
Enhanced LSTM for natural language inference
2013 1419 citations
NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets
2016 1305 citations
Semeval-2016 task 6: Detecting stance in tweets
2014 1229 citations
Sentiment analysis of short informal texts
2014 968 citations
NRC-Canada-2014: Detecting aspects and sentiment in customer reviews

Research Contributions

Developed advanced LSTM models for natural language inference tasks to improve understanding of language semantics.
Enhanced the accuracy and performance of NLP applications such as sentiment analysis and stance detection.
Built state-of-the-art sentiment analysis systems specifically tailored for tweets and short informal texts.
Enabled more accurate and timely analysis of public opinion and social media content.
Contributed to creating benchmark datasets for stance detection and sentiment analysis in social media.
These datasets have facilitated further research and development in social media language understanding.

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