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Scientific Computing and Data Analysis (Computer Vision and Robotics) at Durham University - UCAS

Durham University

Degree level: Postgraduate

Scientific Computing and Data Analysis (Computer Vision and Robotics) (Taught)

Course options

2024 - 20252025 - 2026
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Open days

10Feb
Durham University - Postgraduate Online Open Week
Postgraduate

Course summary

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 Astrophysics, Earth and Environmental Sciences, or Financial Technology)
The MISCADA specialist qualification in Computer Vision and Robotics is designed to equip you with the background knowledge and skills to address some of the biggest research questions in computer vision and robotics, such as how we can develop future mobility solutions which combine autonomy with safety and reliability. The course explores areas such as computer vision, machine learning, robotic motion and planning, as well as reinforcement learning. 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 computer vision and robotics, either in academia or in industry, then this could be the course you’re looking for. 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 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. Computer Vision explores contemporary concepts, approaches and algorithms in computer vision and examines how current research is applied in the industry. Examples of themes include stereo vision, object tracking, real-time processing approaches, scene reconstruction from multiple image, object detection, and applications of computer vision for autonomous navigation. Robotics – Planning and Motion develops your knowledge of key concepts, approaches and algorithms in robotics, and how current research is applied in the industry. Deep Learning for Computer Vision and Robotics explores key concepts, approaches and algorithms for the use of deep machine learning and its application within industry.

Modules

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.

Assessment method

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 methodological academic departments (Mathematical Sciences or Computer Science), or within the computer vision and robotics field, or in close cooperation with our industrial partners.

How to apply

International applicants

If you are an international student who does not meet the requirements for direct entry to this degree, you may be eligible to take a pre-Masters pathway programme at the Durham University International Study Centre.

Entry requirements

All streams require a UK first or upper second class honours degree (BSc) or equivalent In Physics or a subject with basic physics courses OR In Computer Science OR In Mathematics OR In Earth Sciences OR In Engineering OR In any natural sciences 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. Please see the University guidance for information on required English language levels.

English language requirements

Durham University welcomes applications from all students irrespective of background. We encourage the recruitment of academically well-qualified and highly motivated students, who are non-native speakers of English, whose full potential can be realised with a limited amount of English Language training either prior to entry or through pre-sessional and/or in-sessional courses. It is the normal expectation that candidates for admission should be able to demonstrate satisfactory English proficiency before the start of a programme of study, whether via the submission of an appropriate English language qualification or by attendance on an appropriate pre-sessional course. Acceptable evidence and levels required can be viewed by following the link provided.

English language requirements

https://www.durham.ac.uk/study/international/entry-requirements/english-language-requirements/

Fees and funding

Tuition fees

England £14500 Year 1
Northern Ireland £14500 Year 1
Scotland £14500 Year 1
Wales £14500 Year 1
Channel Islands £14500 Year 1
EU £34000 Year 1
International £34000 Year 1

Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .

Additional fee information

The tuition fees shown are for one complete academic year of study and are set according to the academic year of entry. Fees will be subject to an annual inflationary increase and are expected to rise throughout the programme of study. The fee listed above is for the first year of the course only.

Sponsorship information

For more information please see the Durham University website.

Scientific Computing and Data Analysis (Computer Vision and Robotics) at Durham University - UCAS