AI in Engineering MSc - 2027/8
Awarding body
University of Surrey
Teaching institute
University of Surrey
Framework
FHEQ Level 7
Final award and programme/pathway title
MSc AI in Engineering
Subsidiary award(s)
| Award | Title |
|---|---|
| PGDip | AI in Engineering |
| PGCert | AI in Engineering |
Modes of study
| Route code | Credits and ECTS Credits | |
| Full-time | PEA61002 | 180 credits and 90 ECTS credits |
QAA Subject benchmark statement (if applicable)
Other internal and / or external reference points
This programme is subject to approval. This means that it has received initial agreement from the University and is currently undergoing a detailed final approval exercise, through the University's quality assurance processes. These processes are a requirement for all Higher Education Institutions within the UK, to ensure that programmes are of the highest standard. Occasionally there may be instances where the University may delay or not approve the introduction of the programme.
Faculty and Department / School
Faculty of Engineering and Physical Sciences - School of Engineering
Programme Leader
HAGEN-ZANKER Alex (Sch of Eng)
Date of production/revision of spec
08/09/2026
Educational aims of the programme
- Cultivate technical resilience and resourcefulness, ensuring the ability to adapt to the rapid evolution of AI technologies and shifting industrial requirements.
- Develop advanced capabilities and in-depth knowledge of AI applications across the entire engineering lifecycle, from conceptual ideation to operational management.
- Emphasize the role of AI in achieving sustainability goals through data-driven efficiency, resource optimization, and the management of complex physical processes.
- Equip graduates with professional-grade software and data engineering skills to integrate and customize AI models within industrial engineering toolchains.
- Foster the ability to critically evaluate the systemic impact of AI, ensuring deployment meets rigorous ethical, societal, and professional standards.
- Prepare graduates for strategic leadership roles with the technical authority to drive AI-enabled innovation and digital transformation in global engineering firms.
- Promote an interdisciplinary mindset that enables the translation of complex engineering challenges into machine-readable solutions across diverse domains.
Programme learning outcomes
| Attributes Developed | Awards | Ref. | |
| Demonstrate a comprehensive knowledge of mathematics, statistics, and engineering principles at the forefront of AI, informed by a critical awareness of new developments in machine learning and data science. | KC | PGCert, PGDip, MSc | M1 |
| Formulate and analyse complex engineering problems to reach substantiated conclusions, using engineering judgment to work with data that may be uncertain or incomplete within AI-driven models. | CP | PGCert, PGDip, MSc | M2 |
| Select and apply appropriate computational and analytical techniques (e.g., evolutionary algorithms and surrogate modelling) to model complex problems, critically discussing the limitations of the techniques employed. | C | PGCert, PGDip, MSc | M3 |
| Select and critically evaluate technical literature and research repositories to solve complex problems and reproduce state-of-the-art AI methodologies. | KC | PGCert, PGDip, MSc | M4 |
| Design AI-driven solutions for complex problems that evidence originality and meet a combination of societal, user, and commercial needs, while adhering to industry standards. | KCPT | PGCert, PGDip, MSc | M5 |
| Apply an integrated or systems approach to solve complex problems by bridging AI methodologies with standard engineering simulation and operational frameworks. | KCPT | PGCert, PGDip, MSc | C6 |
| Evaluate the environmental and societal impact of AI solutions across the entire life-cycle¿from data acquisition to system decommissioning¿to minimize adverse impacts. | KCP | PGCert, PGDip, MSc | M7 |
| Identify and analyse ethical concerns related to algorithmic bias and automated decision-making, making reasoned choices informed by professional codes of conduct. | KCT | PGCert, PGDip, MSc | C8 |
| Use a risk management process to identify and mitigate technical uncertainties and the effects of uncertainty associated with AI-driven engineering activities. | KCT | PGCert, PGDip, MSc | C9 |
| Adopt a holistic approach to the mitigation of security risks, specifically addressing data integrity, model vulnerability, and cybersecurity in Cyber-Physical Systems. | KCT | PGCert, PGDip, MSc | C10 |
| Adopt an inclusive approach to engineering practice, recognizing the importance of diversity and transparency in the development and deployment of AI systems. | KCPT | PGCert, PGDip, MSc | M11 |
| Use practical laboratory and workshop skills to physically integrate AI-driven systems with engineering hardware, encompassing the installation, calibration, and troubleshooting of sensors and actuators to investigate complex operational problems. | PT | PGCert, PGDip, MSc | M12 |
| Select and apply appropriate engineering technologies and processes, such as professional APIs and cloud-based intelligence, while recognizing their operational limitations. | KCT | PGCert, PGDip, MSc | M13 |
| Discuss the role of quality management systems and continuous improvement in ensuring the reliability, reproducibility, and safety of AI-driven engineering models. | PT | MSc | M14 |
| Apply knowledge of engineering management and commercial context, including intellectual property rights and legal frameworks relevant to AI software and data ownership. | KCT | PGDip, MSc | M15 |
| Function effectively as an individual, and as a member or leader of a multidisciplinary team, demonstrating accountability in the delivery of collaborative AI projects. | PT | PGCert, PGDip, MSc | M16 |
| Communicate effectively on complex engineering matters with technical and non-technical audiences, evaluating the effectiveness of the communication methods used during project defences. | PT | PGCert, PGDip, MSc | M17 |
| Plan and record self-learning and development in the rapidly evolving field of AI in engineering as the foundation for lifelong professional development. | P | MSc | C18 |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Programme structure
Full-time
This Master's Degree programme is studied full-time over one academic year, consisting of 180 credits at FHEQ level 7. All modules are semester based and worth 15 credits with the exception of project, practice based and dissertation modules.
Possible exit awards include:
- Postgraduate Diploma (120 credits)
- Postgraduate Certificate (60 credits)
Programme Adjustments (if applicable)
N/A
Modules
Year 1 (full-time) - FHEQ Level 7
Module Selection for Year 1 (full-time) - FHEQ Level 7
As part of the approval process the following new modules have been developed and will be added to the programme once available:
Engineering Lifecycle and AI
AI Concepts and Methods
Digital Skills for AI Deployment
Conceptual Design and AI-driven Ideation
Optimisation with AI in Engineering
Cyber-Physical Systems and Digital Twins
Dissertation (AI in Engineering)
Opportunities for placements / work related learning / collaborative activity
| Associate Tutor(s) / Guest Speakers / Visiting Academics | Y | |
| Professional Training Year (PTY) | N | |
| Placement(s) (study or work that are not part of PTY) | N | |
| Clinical Placement(s) (that are not part of the PTY scheme) | N | |
| Study exchange (Level 5) | N | |
| Dual degree | N |
Other information
The School of Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This programme is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Digital Capabilities: Students achieve technical mastery in bridging high-level AI toolchains with industrial engineering software. The programme moves beyond theoretical data science to the practical implementation of "living" Digital Twins, real-time data pipelines, and the orchestration of complex AI models within high-fidelity physics solvers.
Employability: The curriculum mirrors the professional R&D environment of leading global firms. Graduates are prepared for leadership roles in advanced simulation and performance engineering. Students develop the authority to justify and advocate for AI-driven strategies to both technical and commercial stakeholders.
Global and Cultural Capabilities: Students examine the ethical governance of AI, addressing algorithmic bias and the societal impact of automated decision-making. By navigating international regulatory frameworks and data standards, graduates are equipped to deploy globally responsible engineering solutions that respect diverse cultural and safety contexts.
Resourcefulness and Resilience: The programme builds technical resilience through rigorous research replication and the management of "failed" models. Students develop the resourcefulness to troubleshoot hardware-software friction in Cyber-Physical Systems, finding alternative technical pathways when faced with the unpredictability of real-world data and physical assets.
Sustainability: Optimization is the core driver of modern sustainability. Students learn to apply AI to minimize material waste, optimize energy consumption in operational assets, and solve resource-scarcity challenges through precise algorithmic refinement and lifecycle analysis.
Quality assurance
The Regulations and Codes of Practice for taught programmes can be found at:
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.