Scientific Computing and Data Analysis (Artificial Intelligence for Engineering)

Entry requirements

All streams require a 2:1 BSc (Honours) degree or international equivalent

• In Engineering OR
• In Computer Science OR
• In 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.

Months of entry

September

Course content

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.

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.

Year 1 modules

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.

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.

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.

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.

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.

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.

In recent years, optional modules have included:

• 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

Information for international students

International students who do not meet direct entry requirements for this degree might have the option to complete an International Foundation Year.

Fees and funding

More information is available here: Tuition fees - how much are they - Durham University