Scientific Computing and Data Analysis (Astrophysics)

Developments in many areas of science and engineering 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 Astrophysics we offer options in Computer Vision and Robotics, Earth and Environmental Sciences, or Financial Technology)

The MISCADA specialist qualification in Astrophysics is designed to equip you with the background knowledge and skills to address some of the biggest research questions in fundamental science, such as how we can use large surveys and supercomputer simulations to probe the nature of dark matter and dark energy. The course explores areas such as stellar structure and evolution, galaxy formation, large-scale structure and simulations of structure formation. 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 astrophysics, either in academia or in industry, then this could be the course you’re looking for.

Course Structure

Core 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 delivers 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 the ethical issues around data and research.

The Project is a substantive piece of research into an unfamiliar area of astrophysics, 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.

Astrophysics teaches you state-of-the-art research and science across a broad range of astrophysics topics, from stellar populations to galaxy formation and high-energy astrophysics. This module introduces the basic research skills needed for postgraduate research.

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 Modelling, Vectorisation and GPU Programming
• Advanced Algorithms and Discrete Systems
• Computational Linear Algebra and Continuous Systems