Artificial Intelligence for Engineering and Design (MSc)

1 Years On Campus Masters Program

University of Bath

Program Overview

This one-year (or two-year with placement) master’s programme equips students with the ability to apply advanced AI, machine learning and big data techniques to real-world engineering and design problems, across sectors from manufacturing to smart cities. It’s ideal for candidates who already hold an engineering, architecture or numerate science degree, and who want to move into roles such as intelligent systems designer, automation specialist or AI-enabled engineer.

Curriculum structure:
Year 1 (Full-time route):

In the taught part of the programme, students begin by engaging with modules such as Artificial intelligence and machine learning for engineering and design, where they learn supervised/unsupervised learning, neural networks and data preparation for design-based challenges.
They also explore Advanced numerical methods, diving into data-mining, modelling, optimisation and applying those methods to creative engineering and design workflows.
Additional modules include Automation, manufacturing and design (looking at digital twins, IoT and manufacturing processes driven by ML) and Smart cities and the Internet of Things (where students apply generative design, urban planning and data from sensor networks).

Project phase (summer onward):

Students complete a substantial Research project (30 credits) in which they apply what they’ve learned to an individual topic of their choosing — for example using AI to solve a sustainability challenge or design optimisation problem in engineering.
For those choosing the two-year programme, they also undertake a Professional placement (up to 12 months) to gain real-world experience and industry contacts before final submission.

Focus areas:
Artificial intelligence and machine learning • Data-driven engineering design • Automation, digital manufacturing & IoT • Smart infrastructure and sustainable systems • Professional skills and research-driven project work.

Learning outcomes:
On successful completion, students will be able to design, implement and evaluate AI-enabled solutions in engineering and design contexts; critically assess and apply data-driven modelling, deep learning and optimisation techniques; demonstrate professional awareness — including ethical implications, sustainability, project management and the societal impact of intelligent systems.

Professional alignment (accreditation):
While the official published page doesn’t yet list a specific professional body accreditation, the programme is positioned by the Faculty of Engineering & Design as bridging engineering and applied AI in a multidisciplinary setting.
Because of this strong engineering-design anchor and the integrated research project, graduates should be well prepared for roles that feed into Chartered Engineer (CEng) pathways or equivalent in engineering practice.

Reputation (employability rankings):
The University of Bath is a well-regarded institution in the UK, particularly in engineering and technology. The programme is advertised as the UK’s first master’s focused purely on applying AI in engineering and design contexts.
By choosing the optional professional placement route, students can significantly enhance their employability and industry networks — a major advantage in the job market today.

Experiential Learning (Research, Projects, Internships etc.)

From the start, students will work with practical, applied tools and real-industry themes. They’ll use specialist high-performance computing (HPC) facilities and labs to build, test and evaluate AI methods in engineering/design contexts. They’ll engage with group-based case studies, and then carry out an individual research project addressing a real engineering/design challenge through AI/ML workflows.
They will also benefit from the University’s advanced digital infrastructure — for example the HPC cloud-environment (“Nimbus”) and teaching clusters that support data-intensive and AI work.

Here are the specific experiential learning features built into the programme:

Laboratory and computing facilities, including high-performance / cloud-based computing environments (HPC, “Nimbus”), designed for large-scale data modelling, simulation and machine-learning workflows.
Modules involving group work and individual work, for example: the module “Artificial intelligence and machine learning for engineering and design” involves working individually and in teams using software tools to apply supervised/unsupervised learning, neural networks, deep learning.
Real-life case studies where students apply AI for engineering/design problems (e.g., in automation/manufacturing, smart cities/IoT) — this gives you experience of industry-style workflows.
A dedicated research project (30 credits) where you identify a challenge in the engineering/design domain, design and carry out an AI/ML-based solution under supervision — this gives you a tangible element you can show to employers.
Optional professional placement version of the programme (2-year with placement) giving the chance to gain full-time industry experience.
Access to specialist labs: e.g., for engineering and design contexts, the programme draws on all four departments of the Faculty of Engineering & Design (Electronic & Electrical, Mechanical, Civil/Architecture, Chemical) meaning you will access cross-discipline tools/facilities.
Support via digital-tools and software environments: you’ll use data-preparation, simulation, modelling and ML/AI tools as part of the modules (for example supervised/unsupervised learning workflows) and benefit from labs with GPU-intensive machines for AI activities.
Emphasis on ethical, sustainability and societal impact aspects of AI in engineering/design — you’ll not only build tools, you’ll consider “how should we do this responsibly, how does it impact society/industry”.
Structured opportunities for teamwork, collaboration and communication throughout modules (seminars, labs, group assignments) so you develop professional skills alongside technical ones.

Progression & Future Opportunities

Graduates of the University of Bath MSc in Artificial Intelligence for Engineering and Design are equipped to step into cutting‑edge roles such as AI/ML Engineer for Industrial Systems, Digital Twin Specialist, Automation & Robotics Designer, or Smart City AI Developer. They gain the skills to apply AI within engineering and design contexts, creating intelligent, efficient and sustainable solutions. 
Here’s how the programme supports professional advancement:

The university’s Careers Service and the Faculty of Engineering & Design’s Postgraduate Employability Team provide tailored one‑to‑one advice, CV and interview support, networking events, and full guidance throughout the course.
The programme is positioned as an industry‑ready qualification—coming from the UK’s first master’s degree focused on applying AI to engineering and design.
Industry engagement is strong: for instance, the university collaborates with organisations like ioMosaic Ltd in a Knowledge Transfer Partnership to embed machine‑learning techniques into engineering & process‑safety systems.
Accreditation and reputation: The university emphasises employability and employer reputation, with accredited courses and strong industry linkages.
Graduation outcomes: Students complete an individual research project in AI/ML for engineering/design, and have optional placement experience (up to 12 months) to apply their learning in real‑world settings.

Further Academic Progression:
After completing the MSc, students may continue into doctoral research (PhD) in areas such as advanced machine learning for design systems, digital twins, or smart infrastructures. Alternatively, they may opt for specialist postgraduate certificates in fields like IoT for engineering, advanced robotics, or sustainable smart‑systems design. For those entering industry first, the strong industry linkages and research credentials also pave the way to leadership or consultancy roles over time.