MSc
Scientific Computing and Data Analysis (Artificial Intelligence for Engineering)
This innovative MSc combines advanced artificial intelligence techniques with engineering applications, preparing graduates to develop and implement AI-driven solutions for complex engineering challenges in industry and research.
How to apply Apply via UCASCourse details
Start date
Degree Type
MSc
Program Code
G5T809
Course length
1 year full-time
Tuition Fees
- Home (Full-time): £14,500 per year
- Overseas (Full-time): £34,000 per year
Overview
Developments in the field of engineering are increasingly driven by experts in computational techniques. Our suite of Masters in Scientific Computing and Data Analysis (MISCADA) offers an application-focused course to deliver these skills with three interwoven strands: Implementation and application of fundamental techniques in an area of specialisation (in addition to AI for Engineering, we offer options in Financial Technology, Astrophysics, Computer Vision and Robotics, or Earth and Environmental Sciences)Computer Science underpinnings of scientific computing (algorithms, data structures, implementation techniques, and computer tool usage)Mathematical aspects of machine learning and the simulation and analysis of mathematical models The MISCADA specialist qualification in Engineering introduces you to engineering applications through a structured program of taught modules and project work. Through lectures, computer labs and projects, you'll learn to: Design and implement AI solutions for engineering problems.Apply deep learning and optimisation techniques to engineering systems.Integrate AI with physical models and engineering principles. Develop robust software implementations. You can find out more here.
Course details
Start date
Degree Type
MSc
Program Code
G5T809
Course length
1 year full-time
Tuition Fees
- Home (Full-time): £14,500 per year
- Overseas (Full-time): £34,000 per year
What you'll study
There’s great synergy between the modules and you will be given plenty of opportunities to put your learning into practice from the start of the course. Our research-led approach allows you to take some of the newest theoretical ideas and learn to apply cutting-edge AI methods to solve real engineering challenges. If you have an undergraduate degree in engineering or a science subject with a strong quantitative element, including computer science and mathematics and want to work at the highest level applying AI methods to engineering problems, either in academia or in industry, then this could be the course you’re looking for.
Core modules
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Engineering Specialization Module: Deep Learning for Engineering
introduces advanced deep learning techniques integrated with engineering methods and domain knowledge. The module focuses on implementing and deploying deep learning models for real-world engineering systems.
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Engineering Specialization Module: Optimisation and Model Predictive Control for Artificial Intelligence
covers methods and implementation of optimisation and model predictive control for AI-driven systems. Emphasis on applications in engineering contexts.
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Introduction to Machine Learning and Statistics
provides knowledge and understanding of the fundamental ideas and techniques in the application of data analysis, statistics and machine learning to scientific data.
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Introduction to Scientific and High Performance Computing
provides knowledge and understanding of paradigms, fundamental ideas and trends in High Performance Computing (HPC) and methods of numerical simulation.
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Professional Skills
provides training in areas such as collaborative coding, project management and entrepreneurship. It will build the skills you need to communicate novel ideas in science, and reflect on ethical issues around data and research.
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The Project
is a substantive piece of research into an area of artificial intelligence for engineering, scientific computing or data analysis, or a related area in cooperation with an industry partner.
Optional modules
- Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
- Advanced Statistics and Machine Learning: Regression and Classification
- Data Acquisition and Image Processing
- Performance Modelling, Vectorisation and GPU Programming
- Advanced Algorithms and Discrete Systems
Learning
This degree is organised by the Department of Computer Science with a specialisation offered by the Department of Engineering. Teaching and learning methods are varied, they include a combination of lectures, practical classes/computer labs, independent study, research and analysis, a project (dissertation) and coursework. Some modules also include group and individual presentations. You will also be given the opportunity to work with a wide variety of high-quality computer kit and software. This includes HPC systems such as GPU clusters, systems with heterogeneous architectures and specialist software installations (such as performance analysis tools), AI tools and data acquisition tools.Assessment
Assessment takes a combination of forms including coursework, presentations and a project which is worth one-third of your total mark. You will complete your dissertation-style project on a topic of your choice from within the contributing academic departments. In the AI for engineering steam this will usually be the Engineering department, or in close cooperation with one of our industrial partners.Entry requirements
To learn more about the qualifications we typically accept, please select your country from the drop-down menu below.
Minimum entry requirements | All streams require a UK first or upper-second-class honours degree (BSc) or equivalent In Engineering ORIn Computer Science ORIn any natural science with a strong quantitative element. We encourage applicants to select a specialization area that aligns with their background. Please note that standard business degrees do not provide the necessary mathematical foundation. Additional requirements Applicants must demonstrate strong programming skills in at least one compiled language, preferably C or C++, although Rust, Java, C#, Fortran, or Pascal are also acceptable. Proficiency in Python may suffice if the applicant has a strong background in their chosen specialization. Those lacking experience in C or C++ are advised to enrol in our pre-sessional course.Additionally, we require knowledge of undergraduate-level mathematics, covering linear algebra, calculus, integration, ordinary and partial differential equations, and probability theory. |
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Typical offer | |
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Fees & Funding
Choose which fees you want to see:
Home / Island students
£14,500 per year
International / EU Students
£34,000 per year
Home / Island students
Part Time - £ per year
International / EU Students
Part Time - £ per year
The tuition fees shown are for one complete academic year of study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).
Find out moreDepartment Information
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Top 10
in the Complete University Guide 202511th
in the Guardian University Guide 2025Joint 6th
for student employability in the Complete University Guide 2025The Department is at the heart of the fast-paced world of applications and algorithms. We maintain an in-depth understanding of the fundamentals of computation and are fully up to speed with the latest technologies that emerge at an ever-increasing rate. Learning from academics who lead cutting-edge research provides valuable insight into high quality projects, and gives our postgraduate community the opportunity to play a role in shaping a future in which crucial developments in society are supported by technological innovation. Taught courses balance fundamental knowledge and an emphasis on programming and mathematical skills with practical applications. The content and structure are such that they suit postgraduates who already have experience in the industry or other employment and want to add a formal qualification to their achievements. Researchers in the Department offer a range of expertise across the computer science spectrum in areas such as artificial intelligence, data science, bioinformatics, high-performance computing, graphics and fundamental algorithms. We ensure our research-led activity does not function in isolation and keep close links with local high-technology industries as well as national and international employers. Those relationships ensure we are at the leading edge of developments across the sector and can revise and adapt the Department’s curriculum to reflect the changes. For more information see our department pages.
Read moreFacilities
The Department is located in a £40 million purpose-built building in the heart of Durham at Upper Mountjoy and features open-plan work areas, breakout spaces for collaboration projects, laboratories and computer rooms. We are fortunate to have supercomputers for High-Performance Computing and for data analysis and machine learning as well as access to several visualisation and data postprocessing laboratories. We are also able to host local computer hardware which give postgraduate researchers a safe environment to test prototype solutions, explore innovative technologies they are developing or to actually design new solutions.
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State of the art building
Our new building features academic offices, offices for research staff and students, open-plan space for students to work, breakout spaces to collaborate, labs, computer rooms and, of course, a café.
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High-Performance Computing Cluster
Durham hosts its own local supercomputers (the Hamilton family) and the DiRAC Cosma machine.
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NVIDIA CUDA Centre (NCC)
This cluster is multifunctional in that it supports all aspects of research and teaching. Students (both masters and PhD) are able to access this cluster for running novel ML studies.
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Career Opportunities
Employability
More Information
Our postgraduates leave with excellent career prospects and go on to diverse careers in academia, business and industry.