Scientific Computing and Data Analysis (Computer Vision and Robotics)

Program Overview

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Scientific Computing and Data Analysis (Computer Vision and Robotics)

Overview

Using the latest scientific computing and data analysis techniques, explore some of the biggest research questions in computer vision and robotics and learn how autonomous cars and intelligent robots interact with the environment around them.

Start Dates

• September 2025

Degree Type

• MSc

Course Length

• 1 year full-time

Location

• Durham City

Programme Code

• G5T509

Course Details

Developments in fields such as 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.

Course Structure

Year 1 Modules

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. Examples of themes include robot classification, position and orientation, typical actuators/sensors, and feedback control, simultaneous localisation and mapping (SLAM), and path planning and obstacle avoidance.
• 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. Examples of themes include scene reconstruction and understanding from multiple images, video or active sensing; simultaneous localisation and mapping (SLAM) from varying sensor inputs; visual odometry from varying sensor inputs; and robotic guidance and control.

Optional 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

Learning

This degree is organised by the Department of Computer Science in collaboration with the Department of Mathematical Sciences, the Department of Physics. 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 include group and individual presentations.

In addition to access to your own small robotics kit, 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 methodological academic departments (Mathematical Sciences or Computer Science), or within the computer vision and robotics field, or in close cooperation with our industrial partners.

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 enroll 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.

Alternative Qualifications

• Other UK qualifications
• EU qualifications
• International qualifications

International Students

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

Home Students

Home students who do not meet our direct entry requirements may be eligible for our Foundation Programme, which offers multidisciplinary programmes to prepare you for a range of specified degree programmes.

Fees and Funding

Full-Time Fees

• Home students: £14,500 per year
• EU students: £34,000 per year
• Island students: £14,500 per year
• International students: £34,000 per year

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.

Career Opportunities

Qualifications in computer science are highly sought after by employers across the globe and an award from our Department provides the academic skills, industry insight, and research-informed approach that sets postgraduates up for careers in a broad range of sectors.

Many postgraduates have gone on to work as software engineers, analysts, consultants, programmers, and developers. Some have founded their own start-ups or work in leading software companies, high-technology consultancies, banking, and finance, retail, engineering, the communications and IT industry.

The Department has strong research links, spanning both industry and government, including the automotive sector with Jaguar Land Rover and Renault, the defense and security sector with QinetiQ and Boeing, with government in the Civil Service and at GCHQ, and in the manufacturing sector with Procter & Gamble. Other high-profile employers include BAE Systems, Google, and BT.

Department Information

Computer Science

The Department of Computer Science’s academic fusion of technology and independent thinking places it firmly at the cutting edge of computer learning. We bring innovative applications together with critical thought processes to give you the skills to play a key role in the data-led future.

The 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.

Facilities

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 gives postgraduate researchers a safe environment to test prototype solutions, explore innovative technologies they are developing, or to actually design new solutions.