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1 Year On Campus Masters Program
University of YorkThe MSc Artificial Intelligence with Large Language Models at the University of York trains students to understand, build and deploy advanced language-model systems. It suits students with a computing or quantitative background who want to specialise in NLP, generative AI and real-world LLM applications.
Curriculum structure
Students begin by studying foundational AI and NLP concepts, learning how models evolved from classical methods to modern architectures such as Transformer models, GPT-style LLMs, and Seq2Seq systems. They then move into advanced LLM modules covering fine-tuning, multimodal LLMs, domain-adaptation, and deployment pipelines, applying these techniques to tasks in text analysis, generation and multimodal reasoning. The programme concludes with the LLM Project/Dissertation, where students design and implement a working system using models such as BERT, GPT-variants, or custom fine-tuned transformers.
Focus areas: “Large Language Models, NLP, Deep Learning, Transformers, Multimodal AI, AI Deployment”
Learning outcomes: “Train and fine-tune LLMs; build NLP and generative-AI systems; deploy transformer-based models; analyse complex language/multimodal data; complete an independent LLM project.”
Professional alignment (accreditation): Designed for careers in NLP engineering, AI product development, data science, generative-AI applications and research roles focused on LLMs.
Reputation (employability rankings): York’s strong research reputation in AI and computing supports excellent employability in cutting-edge AI and language-technology sectors.
Students develop critical analytical skills through structured debates, ethical analysis of real AI case studies, and engagement with interdisciplinary research at the intersection of philosophy and computer science. This applied learning is central to the curriculum:
Case Study Analysis: Examination of real-world AI implementations and their societal impacts.
Research Project: A substantial dissertation on a philosophical problem in AI.
Interdisciplinary Engagement: Collaboration with Computer Science department research.
Ethical Frameworks: Development of practical AI ethics guidelines and policy recommendations.
Analytical Tools: Training in formal logic and philosophical analysis methods for AI systems.
Research Environment: Access to the Department of Philosophy's research clusters and seminars.
Graduates of University of York's MSc Artificial Intelligence with Large Language Models master engineering LLM-based agents, AI search, logic, and governance to innovate responsible AI solutions transforming industries from research to commercial applications. The program embeds employability skills through real industry cases, project management, and risk assessment, preparing alumni for leadership in the fast-evolving language model field. Typical job roles: programming engineer, software developer, AI researcher, systems architect.
University services: Embedded employability training including project management workshops, industry case studies, and skills development in computational thinking and professional presentation.
Employment stats/salary: Strong prospects in high-demand AI sectors with competitive salaries for LLM specialists; 90% of related Computer Science graduates employed post-graduation.
University–industry partnerships: Research-led teaching with real-world projects; collaborations enable hands-on experience with cutting-edge LLM applications.
Long-term accreditation value: Advanced computational and ethical AI skills ensure adaptability and leadership in evolving language model technologies.
Graduation outcomes: Careers in AI development, research, software engineering, and systems design worldwide.
Further Academic Progression: Pursue PhD in AI/LLMs at York, extending dissertation research under expert supervision.
2:2 or equivalent in Computer Science or a relevant discipline. Successful applicants will demonstrate a strong background in the following essential topics: programming, maths and a knowledge of basic algorithmsUSA- a score of 2.7 out of 4.0 is considered comparable to a UK 2:2India- A score of 50% is considered comparable to a UK lower second class (2:2) degreeIELTS (Academic and Indicator)- 6.5, minimum 6.0 in each componentTOEFL- 87, minimum 21 in each componentAI Policy AdvisorTechnology LawyerDigital Regulation Counsel AI Ethics ConsultantAI Governance AnalystRisk and Compliance Manager for tech firms
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