Scientific Computing and Data Analysis: AI Infrastructure Platforms

Entry requirements

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

• In Computer Science OR
• A related degree with a strong computer science component

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.

Please see the University guidance for information on required English language levels.

Months of entry

September

Course content

Developments in fields such robotics, physics, engineering, earth sciences or finance are increasingly driven by experts in computational techniques. Those with the skills to write code for the most powerful computers in the world and to process the biggest data sets in the world can truly make a difference.

Our suite of Masters in Scientific Computing and Data Analysis (MISCADA) offers an application-focused course to deliver these skills with three interwoven strands:

• Computer Science underpinnings of scientific computing (algorithms, data structures, implementation techniques, and computer tool usage)
• Mathematical aspects of data analysis and the simulation and analysis of mathematical models

Implementation and application of fundamental techniques in an area of specialisation (as well as Computer Vision and Robotics we offer options in options in Astrophysics, Engineering, AI Platforms, Financial Technology, or Environmental and Geographic Information Systems).

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 directly translate them into working codes in their respective application areas. If you have an undergraduate degree in a science subject with a strong quantitative element, including computer science and mathematics and want to work at the highest level in high performance computing, either in academia or in industry, then this could be the course you’re looking for.

The MISCADA specialisation in AI Infrastructure Platforms is designed to equip you with the knowledge and technical skills needed to design, operate, and manage the compute platforms powering today’s most advanced AI and simulation workloads. The course covers hardware architecture, software stacks, system monitoring, performance optimization, and data centre operations.

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 C refresher training with an outlook into large-scale code usage. You will also develop wider professional skills in areas such as entrepreneurship, intellectual property and build the skill you will 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 unfamiliar area of robotics, scientific computing or data analysis, or a related area in cooperation with an industry partner. The project will develop your research, analysis and report-writing skills.

Introduction to Software and Hardware infrastructure

equips students with theoretical and practical knowledge of HPC systems for advanced simulation and AI. It covers hardware architecture, software stacks, networking, and data center operations, emphasizing system design, scripting, and maintenance to prepare students for evolving technologies in high-performance computing environments.

Systems Monitoring, Data Centre Security and User Management

provides a comprehensive understanding of data centre operations, focusing on energy use, code performance, user management, and security. Students gain hands-on experience with provisioning and monitoring; developing skills to manage users, optimize performance, and address security challenges in modern data centre environments.

Plus optional modules which may include:

• Advanced Statistical and Machine Learning: Foundations and Unsupervised Learning
• Advanced Statistics and Machine Learning: Regression and Classification
• Data Acquisition and Image Processing
• Performance Engineering and Advanced Algorithms
• Continuous 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