MSc Artificial Intelligence for Engineering

1 Year On Campus Masters Program

University of Hull

Program Overview

This MSc equips students with advanced skills to integrate artificial intelligence (AI) into engineering systems, preparing them to design, control and optimise real-world engineering challenges. It suits those who already have a numerical STEM background (engineering, physics, maths or similar) and who want to move into the intersection of AI and engineering rather than purely software or business analytics.

Curriculum Structure

Year 1 (Full-time, 1 year duration)
In the initial trimester, students build fundamental technical competence: they engage with “Programming for AI and Data Science” to master Python and key AI libraries, and “Numerical Methods for Engineering” to tie engineering mathematics and mechanistic modelling to machine-learning workflows. At the same time, the module “Understanding Artificial Intelligence” introduces supervised learning, convolutional neural networks (CNNs), frameworks like Keras/TensorFlow, and ethical/legal implications.
In the next phase, they move to “Applied Artificial Intelligence,” where deep learning and real-problem applications (e.g., classification, bias and equality) are studied, and “AI for Optimal Control,” which drills into AI in engineering domains such as aircraft, ships or autonomous driving.
Finally, students undertake the “Research and Application in Artificial Intelligence and Data Science” and the large “Artificial Intelligence and Data Science Research Project” (60 credits) which gives them the freedom to define and carry out research or an industrial-linked project in an area aligned with their background and interests.

Focus areas

Predictive modelling & control systems · Deep learning for engineering applications · Numerical/algorithmic methods in engineering · Ethical & legal use of AI in engineering contexts

Learning outcomes

Graduates will be able to design AI-driven control systems in engineering, bridge engineering mathematics with machine-learning workflows, evaluate the societal and technical implications of AI in engineering, and carry out independent empirical research or industry projects.

Professional alignment (accreditation)

While specific accreditation details for this programme (e.g., by a professional engineering institution) are not listed in the publicly available source, the University emphasises industry-relevant skills in AI + engineering and access to its dedicated centre (DAIM) for AI/data science research and teaching.

Reputation (employability rankings)

The University’s teaching facility (4.5 million GBP centre) and dedicated centre for AI/data (DAIM) reflect strong institutional investment. The programme webpage states that there is a “growing demand for skilled professionals who can apply AI in engineering” and that graduates will be “in high demand”.

Experiential Learning (Research, Projects, Internships etc.)

Students of the MSc Artificial Intelligence for Engineering at the University of Hull gain hands-on experience that bridges advanced AI techniques with real engineering applications. From the outset, they develop practical skills in coding, data modelling, and system control using high-end computing facilities. The programme is designed around applied learning — students spend significant time working in specialist labs, participating in projects, and engaging in research that simulates real-world engineering challenges.

To provide a deeper sense of how experiential learning takes shape within the programme:

Students have access to the University’s £4.5 million Centre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), featuring more than 250 high-spec PCs configured for programming, AI modelling, and engineering simulation.
Core software tools such as Python, TensorFlow, and Keras are used extensively in modules like Understanding Artificial Intelligence, Applied Artificial Intelligence, and AI for Optimal Control.
Coursework includes both individual and group projects, with modules assessed through portfolio work, presentations, and reports.
Each student completes a major independent research project (60 credits), developing and executing an AI solution to a practical engineering problem under academic supervision.
Modules such as Numerical Methods for Engineering train students in scientific computing and system modelling, providing essential grounding for AI-driven engineering design.
The Research and Application in Artificial Intelligence and Data Science module allows students to apply AI in industry-relevant contexts such as sustainability, healthcare, creative industries, and environmental systems.
The AI for Optimal Control module focuses on engineering control systems, such as aircraft, autonomous vehicles, and marine systems, integrating AI with applied mechanics.
Students also benefit from access to the University’s Supercomputing facilities, the Superlab, and the renowned Brynmor Jones Library, which supports both technical and interdisciplinary research.

Progression & Future Opportunities

Graduates of the MSc Artificial Intelligence for Engineering at the University of Hull are well prepared to apply advanced AI and data science techniques to complex engineering problems, opening pathways to careers as Engineers, Control and Automation Engineers, IoT or Embedded Systems Engineers, and Data Scientists in engineering-driven industries.

Progression & Future Opportunities:

University services: Students benefit from the University’s Careers and Employability Service, which provides one-to-one career guidance, access to the Handshake job portal, opportunities for placements and part-time work, CV and interview workshops, and lifelong alumni career support.
Employment stats: According to the University’s latest Student Outcomes report, 76% of Hull graduates secure degree-level employment or progress to further study within 15 months of graduation.
University–industry partnerships: The programme is delivered through the University’s £4.5 million Centre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), where students may undertake research projects in collaboration with industry partners such as the NHS, KCOM, and Lampada Digital Solutions.
Long-term accreditation value: The MSc is a fully accredited 180-credit postgraduate taught degree, meeting UK and international academic standards for advanced engineering education. This qualification is recognised globally, enhancing long-term career mobility.
Graduation outcomes: The curriculum develops high-level technical expertise through modules such as AI for Optimal Control—focusing on neural network systems for applications in aviation, automotive, and maritime control—and Numerical Methods for Engineering, which underpins the use of machine learning for engineering modelling.

Further Academic Progression:
Graduates may progress to research degrees such as an MPhil or PhD in Artificial Intelligence, Control Systems, or Advanced Engineering. Alternatively, they may pursue specialised professional qualifications in areas such as autonomous systems, robotics, or advanced data science, expanding their expertise and leadership potential within the field.