The MSc in Mathematics and Foundations of Computer Science, run jointly by the Mathematical Institute and the Department of Computer Science, focuses on the interface between pure mathematics and theoretical computer science.
The mathematical side concentrates on areas where computers are used, or which are relevant to computer science, namely algebra, general topology, number theory, combinatorics and logic. Examples from the computing side include computational complexity, concurrency, and quantum computing. Students take a minimum of five options and write a dissertation.
The course is suitable for those who wish to pursue research in pure mathematics (especially algebra, number theory, combinatorics, general topology and their computational aspects), mathematical logic, or theoretical computer science. It is also suitable for students wishing to enter industry with an understanding of the mathematical and logical design and concurrency.
The course will consist of examined lecture courses and a written dissertation. The lecture courses will be divided into two sections:
Each section shall be divided into schedule I (basic) and schedule II (advanced). Students will be required to satisfy the examiners in at least two courses taken from section B and in at least two courses taken from schedule II. The majority of these courses should be given in the first two terms.
During Trinity term and over the summer students should complete a dissertation on an agreed topic. The dissertation must bear regard to course material from section A or section B, and it must demonstrate relevance to some area of science, engineering, industry or commerce.
It is intended that a major feature of this course is that candidates should show a broad knowledge and understanding over a wide range of material. Consequently, each lecture course taken will receive an assessment upon its completion by means of a test based on written work. Students will be required to pass five courses, that include two courses from section B and two at the schedule II level - these need not be distinct - and the dissertation.
The course runs from the beginning of October through to the end of September, including the dissertation.
This Masters will prepare you in the physical sciences and mathematics for a research career in climate, atmospheric or environmental sciences. It ideally bridges the gap between undergraduate studies in physical/natural sciences and engineering, and study for a PhD.
Alternatively, if you decide to leave academia, the highly transferable skills gained from this course could lead to a research role in industry or government.
Gain a broad overview of physical problems in climate and atmospheric science, together with a sound physical understanding of natural processes. Alongside this, develop highly transferable skills to conduct research in these subjects with a strong emphasis on quantitative data analysis and physical and numerical modelling.
A career in scientific research is always interesting – sometimes exciting – but might not suit everyone. This course provides an excellent opportunity to get a taste of postgraduate research study and decide whether it is really the career for you.
Interact with academics who are at the forefront of major global issues. Leeds is a leading centre of excellence across both the physical science of the climate and atmosphere science, and the resultant socio-economic impacts and processes:
Institute for Climate and Atmospheric Science (ICAS) is the UK’s most diverse academic institute for atmospheric research.
Priestley International Centre for Climate Change (PICC) a world-leading centre for policy-relevant, solution-driven climate research.
Centre for Polar Observation and Modelling (CPOM) is a research centre that studies processes in the Earth's polar latitudes that may affect the Earth's albedo, polar atmosphere and ocean circulation, and global sea level.
Develop your research skills – you will be regarded as a researcher in the School and expected to work closely with ICAS staff as well as presenting at the annual ICAS Science Conference along with academics and doctoral researchers.
Continue on to a PhD, or move into a research role in industry or government. Highly numerate graduates with training in independent research are widely sought after in many sectors.
The School's £23m building gives you access to world-class research, teaching and laboratory facilities, and dedicated computer facilities – many of which will be available to you throughout your studies.
You will be regarded as a researcher within the School and be expected to work closely with ICAS staff as well as presenting at the annual ICAS away day along with academic staff and doctoral researchers.
Be taught by staff from across the School, primarily from ICAS. Your programme manager is Dr Ryan Neely (ICAS) who also teaches as well as regularly supervises your research project and provides tutorial support.
You'll undertake 180 credits worth of work during the year, based on 4 super-modules, each of which is made up of several components.
Two of these super-modules (Quantitative Skills and Specialist Knowledge) allow you to choose from an expansive range of 'atmospheric' and/or 'climate science' options.
You can choose modules based on the direction of your research project and your first degree, as well as any other previous experience.
These are typical modules/components studied and may change from time to time. Read more in our Terms and conditions.
You’ll be taught through classwork, research seminars, lectures, tutorials, poster presentation, fieldwork and tutorials, group work and/or individual.
For your dissertation project, instead of the traditional thesis, you’ll submit a manuscript suitable for submission to an academic journal. This aims to teach the key transferable skill of communicating results professionally and efficiently, and increase the frequency of publication of students’ research.
The School’s £23m building gives you access to world-class research, teaching and laboratory facilities. You'll also have access to a dedicated computer suite throughout your studies.
Your dissertation project accounts for a significant part of your assessment.
You’re also assessed on work you do in course, for example through field notebooks, project proposals, seminars, submission of a computer project and a literature-based survey.
Students carry out research-directed work, implementing new developments and joining existing and new collaborations with agencies such as the Meteorological Office, British Antarctic Survey and the National Centre for Atmospheric Science. Many students perform field projects in conjunction with international field campaigns.
You will be prepared for a research career, usually onwards to a PhD but this could also lead to a research role in government or industry.
Traditionally a very high proportion of our students go on to further PhD study in climate or atmospheric science. In fact, over the last three years all our students who applied for funded PhD positions at Leeds were successful, with several of them holding multiple offers of fully funded research studentships.
While others have obtained places at Cambridge, Reading, Edinburgh, and UEA, among others.
Highly numerate graduates with training in independent research are widely sought after. And our graduates who choose to leave academia have strong employment prospects – landing jobs with national agencies, environmental consultancies, wind-power companies and the insurance sector.
This degree provides in-depth training for students interested in a career in industry or in research-oriented institutions focused on image and video analysis, and deep learning.
State-of-the-art computer-vision and machine-learning approaches for image and video analysis are covered in the course, as well as low-level image processing methods.
Students also have the chance to substantially expand their programming skills through projects they undertake.
Read about the experience of a previous student on this course, Gianmarco Addari.
This programme is studied full-time over 12 months and part-time from 24 to 60 months. It consists of eight taught modules and a standard project.
Example module listing
The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
This programme in Computer Vision, Robotics and Machine Learning aims to provide a high-quality advanced training in aspects of computer vision for extracting information from image and video content or enhancing its visual quality using machine learning codes.
Computer vision technology uses sophisticated signal processing and data analysis methods to support access to visual information, whether it is for business, security, personal use or entertainment.
The core modules cover the fundamentals of how to represent image and video information digitally, including processing, filtering and feature extraction techniques.
An important aspect of the programme is the software implementation of such processes. Students will be able to tailor their learning experience through selection of elective modules to suit their career aspirations.
Key to the programme is cross-linking between core methods and systems for image and video analysis applications. The programme has strong links to current research in the Department of Electronic Engineering’s Centre for Vision, Speech and Signal Processing.
To support your learning, we hold regular MSc group meetings where any aspect of the programme, technical or non-technical, can be discussed in an informal atmosphere. This allows you to raise any problems that you would like to have addressed and encourages peer-based learning and general group discussion.
We provide computing support with any specialised software required during the programme, for example, Matlab. The Faculty’s student common room is also covered by the University’s open-access wireless network, which makes it a very popular location for individual and group work using laptops and mobile devices.
Specialist experimental and research facilities, for computationally demanding projects or those requiring specialist equipment, are provided by the Centre for Vision, Speech and Signal Processing (CVSSP).
Computer vision specialists are be valuable in all industries that require intelligent processing and interpretation of image and video. This includes industries in directly related fields such as:
Studying for Msc degree in Computer Vision offers variety, challenge and stimulation. It is not just the introduction to a rewarding career, but also offers an intellectually demanding and exciting opportunity to break through boundaries in research.
Many of the most remarkable advancements in the past 60 years have only been possible through the curiosity and ingenuity of engineers. Our graduates have a consistently strong record of gaining employment with leading companies.
Employers value the skills and experience that enable our graduates to make a positive contribution in their jobs from day one.
We draw on our industry experience to inform and enrich our teaching, bringing theoretical subjects to life. Our industrial collaborations include:
This course gives an excellent preparation for continuing onto PhD studies in computer vision related domains.
We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.
In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.
This programme prepares you to become a proficient data scientist, developing your specialist knowledge in subjects that are crucial for mastering the vast and ever-more complex information landscape that is characteristic of modern, digitally empowered organisations. You will gain advanced knowledge in areas such as data mining, machine learning, and data visualisation, including state of the art techniques, programming toolkit, and industrial and societal application scenarios. We offer a conversion module that contains a series of optional tutorials at the beginning of semester one to equip students with the essential background material in computational thinking, maths and statistics, which are essential for this course. These tutorials are available to all students on the course, but may be particularly recommended for those graduates without a computer science background.
Semester one: Machine Learning; Foundations of Data Science; Data Visualisation; Cloud Application Development; Foundations of Artificial Intelligence; Evolution of Complexity; Intelligent Agents; Foundations of Web Science.
Semester two: Data Mining; Advanced Databases; Advanced Intelligent Agents; Advanced Machine Learning; Computational Finance; Open Data Innovation; Semantic Web Technologies; Simulation Modelling for Computer Science; The Science of Online Social Networks; Computational Biology; Applied Statistical Modelling; Advanced Computational Methods II.
Plus three-month independent research project culminating in a dissertation.