University of Hull

What you'll study

This programme emphasises the application of artificial intelligence and machine learning methods to engineering challenges. You will cover foundational and advanced topics through a mix of lectures, practical labs and project work, concluding with a substantial individual or group MSc project tackling a real engineering problem.

• Core AI and machine learning – supervised and unsupervised learning, neural networks, deep learning, model evaluation and deployment.
• Intelligent systems for engineering – application of AI techniques to control, monitoring and optimisation in engineering domains.
• Robotics and autonomous systems – perception, localisation, motion planning and integration of hardware and software for robotic platforms.
• Control systems and signal processing – classical and modern control, sensor fusion, filtering and real-time data processing for engineering systems.
• Software engineering for AI – system architecture, version control, testing and deployment practices for production-ready intelligent systems.
• Optimisation and numerical methods – algorithmic optimisation, metaheuristics and their role in engineering design and operation.
• Ethics, safety and regulation – responsible AI, safety-critical systems, verification and standards relevant to engineering applications.
• Research methods and project – research skills, literature synthesis and a major project that applies AI to an engineering problem, often undertaken with industrial partners or within research groups.

The programme typically combines taught modules in the first two terms followed by an independent research or industry-linked project. Laboratory sessions make use of specialist facilities and computing infrastructure to support practical work in machine learning, robotics and embedded systems.

Entry requirements

Applicants are normally expected to hold a good honours degree (equivalent to a UK 2:1) in engineering, computer science, mathematics, physical sciences or a closely related subject. Candidates with a lower-class undergraduate degree (for example a 2:2) but with substantial relevant professional experience or evidence of prior learning may be considered.

Applicants whose first language is not English will need to demonstrate proficiency in English. Typical requirements include an IELTS score that meets the University's postgraduate threshold (or equivalent). Additional pre-sessional English support or bridging modules may be recommended in some cases.

Shortlisted candidates may be asked for a CV, references and, if applicable, examples of prior project work or a portfolio. Prior programming experience (in languages such as Python, MATLAB or C++) and familiarity with mathematics for engineering are strongly recommended.

Career prospects

Graduates of this MSc go on to roles that combine engineering discipline knowledge with AI expertise. Common career paths include:

• Machine learning engineer or data scientist working on engineering datasets and predictive maintenance.
• Control systems or automation engineer integrating intelligent control and optimisation into plant and machinery.
• Robotics engineer developing perception, planning and control for autonomous systems in manufacturing, maritime or logistics.
• Embedded systems developer implementing AI on edge devices and industrial controllers.
• Research and development roles in industry or further study towards a PhD in AI, robotics or engineering disciplines.

The programme’s industrially relevant project work and links with regional and national engineering sectors enhance employability, with alumni working in sectors such as energy, maritime, manufacturing, transport and technology services.

Why study at University of Hull

Hull offers a research-led environment with strengths in applied engineering and data-driven technologies. The University provides access to specialist laboratories, robotics and sensor facilities, and high-performance computing to support practical AI work. Teaching is delivered by academics active in AI, control and engineering research, ensuring curriculum content reflects current practice and emerging challenges.

Students benefit from opportunities to work on industry-linked projects and collaborations with local and national engineering employers, enhancing real-world experience and professional networks. The University’s supportive postgraduate community, careers and enterprise services, and practical focus make it a suitable place to develop the technical and professional skills needed to apply AI successfully in engineering contexts.