We are building a community of researchers working on current and future challenges within financial computing and analytics. With three collaborating universities, our PhD programme is rich in opportunities for engineers and scientists to gain confident skills and knowledge within their chosen field of research. Our students work on an applied research project with one of our industry partners.
Modes and duration
Tuition fees (2019/20)
Note on fees: The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website. The MRes may only be taken as part of the MRes + MPhil/PhD Financial Computing and is only available full-time (1 year). The MPhil/PhD may be taken as a standalone programme and is available full-time (3 years) or part-time (5 years).
Fee deposit: All full-time students are required to pay a fee deposit of £2,000 for this programme. All part-time students are required to pay a fee deposit of £1,000.
A minimum of an upper second-class UK Bachelor’s degree in a relevant discipline, or an overseas qualification of an equivalent standard. Work experience may also be taken into account.
English language requirements
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
The English language level for this programme is: Good
Further information can be found on our English language requirements page.
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
International applicants can find out the equivalent qualification for their country by selecting from the list below.
Select your country:
Challenging and original research projects are undertaken on the PhD programme, with support from the highest standard of academic advisors. Students follow specialist lines of research at a doctorate level and apply their work with innovative technologies.
Financial Computing and Analytics encompasses a wide range of research areas including mathematical modelling in finance, computational finance, financial IT, quantitative risk management and financial engineering. PhD research areas include stochastic processes, quantitative risk models, financial ecometrics, software engineering for financial applications, computational statistics and machine learning, network, high performance computing and statistical signal processing.
- Bioinformatics: protein structure; genome analysis; transmembrane protein modelling; de novo protein design methods; exploiting grid technology; mathematical modelling of biological processes
- Financial computing: software engineering; computational statistics and machine learning; mathematical modelling
- Human centred systems: usability of security and multimedia systems; making sense of information; human error and cognitive resilience
- Information security: cryptology; digital watermarking; cryptoanalysis; steganography
- Intelligent systems: knowledge representation and reasoning; machine learning
- Media futures: digital rights management; information retrieval; computational social science; recommender systems
- Networks: internet architecture; protocols; mobile networked systems; applications and evolution; high-speed networking
- Programming Principles, Verification and Logic’: logic and the semantics of programs; automated tools for verification and program analysis; produce mathematically rigorous concepts and techniques that aid in the construction and analysis of computer systems; applied logic outreach in AI, security, biology, economics
- Software systems engineering: requirements engineering; software architecture; middleware technologies; distributed systems; software tools and environments; mobile computing
- Virtual environments: presence, virtual characters; interaction; rendering; mixed reality
- Vision and imaging science: face recognition; medical image analysis; statistical modelling of colour information; inverse problems and building mathematical models for augmented reality; diffusion tensor imaging.
Year One: Master's Study
This programme allows a high level of flexibility in its structure, with the involvement of departments across the three participating partner universities.
Students are required to take exams for three taught modules (45 credits) and two professional development modules (30 credits). A 12-month project (105 credits) is also undertaken. This work leads to a dissertation of approximately 20,000 words, which is completed by the end of the first year.
Modules are chosen to complement the student's skills. The PhD centre aims to bring the students to an excellent level in finance, analytics (maths and statistics) and programming during the first year, enabling progression toward a PhD. Additional modules of interest can be undertaken at any of the three collaborating universities, throughout the whole period of study.
- PhD modules
- Professional development modules at UCL's specialist training centre
Years Two–Four: Applied Research
From year two, students concentrate on their research and take a unique opportunity to apply their work. A research project in undertaken during a placement arranged with one of our industry partners. This applied research is agreed between the student and the centre, with support given to find a good match for the placement.
Contact is maintained between the student and their supervisor, flexible to their needs and progress.
Lectures are attended as necessary to support the student's research, but their are no mandatory modules required at this stage. Viva exams are completed to gain the PhD qualification.
Each student on the programme has:
- an academic supervisor (from UCL, LSE or Imperial College) and an industry advisor (a partner bank, fund, analytics company or Thomson Reuters)
- a research project in financial IT, computational finance or financial engineering with an industrial partner
- a Master's programme comprising of a bespoke set of graduate level modules from UCL, LSE and IC
- training in industry software, such as Rauters Eikon through UCL's virtual trading floor
- a significant period of at least six months of industrial placement as agreed between the academic supervisor and industrial advisor
- a short period at an international academic centre, such as the Quantitative Products Laboratory in Berlin, Carnegie Mellon University or Tsinghua University in Beijing
Scholarships relevant to this department are displayed below.
- Applications now closed for 2019/20
- Fees, maintenance and travel (Duration of programme)
- Based on academic merit
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
UCL Computer Science graduates secure careers in a variety of organisations including global IT consultancies, City banks and specialist companies in manufacturing industries. The Department takes pride in helping students in their career choices and offers placements and internships with numerous start-up technology companies, including those on Silicon Roundabout, world-leading companies such as Google, Skype and Facebook, and multi-national Finance companies including Morgan Stanley, Deutsche and JP Morgan. Our graduates secure roles such as applications developers, information systems managers, IT consultants, multimedia programmers, software engineers and systems analysts in companies such as Microsoft, Cisco, Bloomberg, PwC and IBM.
UCL Computer Science Research Students' employability is greatly enhanced by working alongside world-leading researchers in cutting-edge research areas such as virtual environments, networked systems, human-computer interaction and financial computing. Computer Science enjoys the UCL multi-disciplinary tradition and shares ideas and resources from across the departments of Faculty of Engineering and beyond. Our alumni have gone on to find work, or found their own successful start-up companies, because they have an excellent understanding of the current questions which face industry and have the skills and the experience to market innovative solutions.
UCL Computer Science is located in the heart of London and subsequently has strong links with industry. We regularly welcome industry executives to observe students' project presentations, we host networking events with technology entrepreneurs (many of which are Computer Science Alumni) and companies sponsor our student prizes. Students also take the advantage of a location close to the City and Canary Wharf to work on projects with leading global financial companies. London is also home to numerous technology communities, for example the Graduate Developer Community, who meet regularly and provide mentors for students interested in finding developer roles when they graduate.
Why study this degree at UCL?
The centre for doctoral training in Financial Computing and Analytics was established at UCL in collaboration with academic partners at the London School of Economics and Imperial College London, supported by partnerships with 20 leading financial institutions. It is the first major collaboration between the financial service industry and academia.
The PhD programme is unique in enabling students to gain the skills required for jobs in the financial services industry or in large analytics companies. The course provides unrivalled contacts, with access to top financial industry professionals and the best academic resources.
Department: Computer Science
Application and next steps
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Deadlines and start dates are usually dictated by funding arrangements so check with the department or academic unit to see if you need to consider these in your application preparation. In most cases you should identify and contact potential supervisors before making your application. For more information see our How to apply page.
For more information see our Applications page.Apply now