University of York

What you'll study

This integrated MEng course builds from fundamental computer science in the first years to advanced AI techniques and systems in later years, with a supervised industrial placement typically taken in the penultimate year. Teaching combines lectures, practical laboratory classes, group coursework, and a substantial individual project at master's level.

• Year 1–2 (Foundations): programming and software engineering, discrete mathematics, data structures and algorithms, computer systems and architecture, databases, and introductory machine learning and data science.
• Intermediate years: deeper algorithmics, probabilistic modelling, optimisation, machine learning, neural networks, natural language processing, computer vision, and modules on ethics and the social implications of AI.
• Industry placement year: a full-year, assessed placement in industry or a substantial approved professional experience that develops applied skills, project management and domain knowledge. The department provides placement preparation and employer links.
• Final year (MEng level): advanced AI topics and specialist options such as reinforcement learning, deep learning for vision and language, robotics and autonomy, scalable systems and distributed computing, plus a major independent research or development project supervised by academic staff.
• Practical features: hands-on coursework using modern AI toolchains (Python, TensorFlow/PyTorch, relevant libraries), access to university computing facilities and HPC resources, and opportunities to collaborate with research groups on applied projects.

Entry requirements

Applicants are expected to have a strong background in mathematics and demonstrable ability in computing. Typical offers require high-level qualifications in relevant subjects; successful candidates commonly hold A-levels (or equivalent) including mathematics and often further mathematics, or strong performance in an international equivalent (such as the IB with higher-level mathematics). Admissions decisions also consider personal statements, references and any relevant programming experience or projects.

International applicants must meet the University of York's English language requirements. Mature applicants and those with non-traditional qualifications are encouraged to apply and will be considered on the basis of prior study, work experience and other evidence of potential to succeed on the programme.

Career prospects

Graduates leave equipped for roles across the tech sector and beyond. Common career paths include AI/ML engineer, data scientist, research engineer, software developer, systems architect, robotics engineer, and technical consultant. The course is also suitable preparation for doctoral study in computer science or artificial intelligence.

The year in industry gives direct workplace experience and often leads to graduate job opportunities with the placement employer or through the department's employer network. York's central careers service and departmental links support CV development, interview preparation and employer engagement.

Why study at University of York

• Research-informed teaching: the Department of Computer Science conducts active research across AI, machine learning, robotics, formal methods and human-centred computing, and teaching draws on current research themes.
• Industry connections and placement support: established links with technology companies and public-sector organisations help secure industry placements and project collaborations.
• Facilities and computing environment: students have access to specialist AI labs, high-performance computing resources and a software-rich teaching environment that supports practical AI and data-intensive work.
• Supportive environment: a close-knit department with personal tutor systems, academic mentoring and dedicated careers support for computing students.
• Location and student life: York offers a compact, historic city environment with strong student services, cultural amenities and good transport links, complemented by campus resources for learning and extracurricular activities.