Mathematical Modelling

Study mode:On campus Study type:Full-time Languages: English
Local:$ 13.1 k / Year(s) Foreign:$ 25.8 k / Year(s) Deadline: Jul 28, 2026
16 place StudyQA ranking:5579 Duration:1 year

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The BSc Mathematical Modelling at University College London offers students an in-depth understanding of how mathematics can be applied to solve complex problems across various disciplines. This interdisciplinary programme provides a solid foundation in mathematical methods, computational techniques, and their applications in real-world scenarios, equipping graduates with the skills needed to address challenges in industry, science, engineering, finance, and technology. Throughout the course, students will explore topics such as differential equations, statistics, optimization, computational modeling, and data analysis, gaining the ability to develop innovative solutions to practical problems. The curriculum is designed to combine theoretical knowledge with practical experience, including hands-on projects, laboratory work, and collaborative activities that foster critical thinking and problem-solving skills. Students will also have opportunities to participate in research projects and internships, enhancing their understanding of current industry practices and trends. The programme emphasizes the importance of effective communication and teamwork, preparing students for careers in a diverse range of sectors. With a strong focus on mathematical rigor and computational proficiency, graduates of this programme are well-equipped for employment in areas such as data science, financial modeling, operational research, engineering, and scientific research. UCL's vibrant academic environment, cutting-edge facilities, and close ties with industry partners ensure that students receive a comprehensive and innovative education. Upon graduation, students will have the analytical and technical skills necessary to contribute meaningfully to advancements in technology and science, making a positive impact on society. The programme also provides a pathway for those interested in pursuing postgraduate studies in mathematics, statistics, data science, and related fields, opening doors to advanced research and specialist careers.

Students develop an understanding of the processes undertaken to arrive at a suitable mathematical model and are taught the fundamental analytical techniques and computational methods used to develop insight into system behaviour. The programme introduces a range of problems - industrial, biological and environmental - and associated conceptual models and solutions.

Students undertake modules to the value of 180 credits.

The programme consists of five core modules (75 credits), three optional modules (45 credits), and a research dissertation (60 credits). 
The part-time option normally spans two years. The eight taught modules are spread over the two years. The research dissertation is taken in the summer of the second year.

Core modules

  • Advanced Modelling Mathematical Techniques
  • Nonlinear Systems
  • Operational Research
  • Computational and Simulation Methods
  • Frontiers in Mathematical Modelling and its Applications

Optional modules

  • Asymptotic Methods & Boundary Layer Theory
  • Biomathematics
  • Cosmology
  • Evolutionary Game Theory and Population Genetics
  • Financial Mathematics
  • Geophysical Fluid Dynamics
  • Mathematical Ecology
  • Quantitative and Computational Finance
  • Real Fluids
  • Traffic Flow
  • Waves and Wave Scattering

Dissertation/report

All MSc students undertake an independent research project, which culminates in a dissertation of approximately 15,000-words and a project presentation.

Teaching and learning

The programme is delivered through seminar-style lectures and problem and computer-based classes. Student performance is assessed through a combination of unseen examination and coursework. For the majority of courses, the examination makes up between 90–100% of the assessment. The project is assessed through the dissertation and an oral presentation.

A minimum of an upper second-class Bachelor's degree in a relevant discipline from a UK university or an overseas qualification of an equivalent standard.

Tuition fees for the Mathematical Modelling program at University College London vary depending on the student's domicile status. For UK students, the annual tuition fee is approximately £9,250, which is consistent with the standard undergraduate fee for UK residents. International students enrolled in this program can expect to pay around £20,870 per year, reflecting the typical cost for international undergraduate studies at UCL. These fees cover access to lectures, seminars, and comprehensive support services provided by the university. In addition to tuition, students should budget for living expenses in London, which can range from £10,000 to £15,000 per year, depending on accommodation choices and personal spending habits. UCL offers a range of financial aid options, including scholarships, bursaries, and grants, to support eligible students. Scholarships are often awarded based on academic excellence, and some are specifically targeted at international students to encourage diversity and inclusion. Students are encouraged to apply early for financial assistance, as funds are limited and competitive. UCL also collaborates with external organizations to provide private funding opportunities, fostering a diverse and inclusive academic environment. For UK students, there are government-backed student loans, such as the Student Finance England (SFE), which cover tuition fees and living costs, repayable after graduation based on income levels. International students may consider private loans or family support, and some may qualify for external scholarships offered by their home countries. Students should also budget for additional costs such as textbooks, study materials, and transport. The university recommends planning finances carefully before embarking on the course to ensure a smooth academic journey. Overall, financing a Mathematical Modelling degree at UCL requires careful planning and utilization of available financial support systems to make higher education accessible and affordable for all students.

Mathematical Modelling at University College London offers students a comprehensive education in applying mathematical techniques to real-world problems across various disciplines. The program aims to develop advanced analytical skills, problem-solving abilities, and computational proficiency, preparing graduates for careers in industries such as finance, engineering, data science, technology, and research. The curriculum covers fundamental topics including differential equations, probability theory, optimization, numerical methods, and statistical analysis, emphasizing their application to practical challenges. Students engage with both theoretical concepts and practical tools, often utilizing software such as MATLAB, Python, and R to carry out simulations and data analysis. The program fosters critical thinking and innovative approaches to complex problems, encouraging teamwork, communication, and ethical considerations in mathematical modelling. Typically, the program integrates lectures, tutorials, coursework, projects, and possibly industry placements to ensure a well-rounded educational experience. Graduates of the course gain the skills needed to develop models that predict outcomes, optimize processes, analyze data sets, and inform decision-making in diverse sectors. The course is designed for students with strong mathematical backgrounds who are interested in digital transformation, technological innovation, and data-driven solutions. Entrance requirements generally include a solid foundation in mathematics and related subjects at the A-level or equivalent. The university’s location in London provides students with access to vibrant industry networks and research collaborations, enhancing employability prospects post-graduation. Overall, the Mathematical Modelling program at UCL offers an intensive, multidisciplinary approach to mathematics with direct applications in scientific research and industry, equipping students with both theoretical knowledge and practical expertise for their future careers.

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