The MPhil in Scientific Computing at the University of Cambridge is a highly interdisciplinary program designed to equip students with advanced computational skills and a deep understanding of scientific principles across various disciplines. The course aims to prepare graduates for research or professional careers in areas that require sophisticated numerical methods, algorithm development, high-performance computing, and data analysis. The program covers a broad spectrum of topics including mathematical modeling, scientific programming, parallel computing, data visualization, and scientific data management. Emphasizing both theoretical foundations and practical applications, it provides students with hands-on experience through projects, coursework, and collaborations with leading research groups. Students will develop expertise in programming languages such as Python, C++, and MATLAB, as well as in using cutting-edge software and hardware for scientific simulation and data analysis. The curriculum is designed to foster problem-solving skills, critical thinking, and innovative approaches to complex scientific challenges. Coursework is complemented by seminars, workshops, and lectures delivered by world-renowned researchers and industry experts, offering valuable insights into current trends and future directions in scientific computing. The program encourages active participation in research projects, often in collaboration with Cambridge's renowned research institutes, providing students with an excellent opportunity to contribute to real-world scientific advancements. Graduates of the MPhil in Scientific Computing are well-positioned to pursue PhD studies, careers in academia, or roles in industry sectors such as technology, pharmaceuticals, finance, and engineering, where computational expertise is increasingly in demand. Admission requirements typically include a strong academic background in computer science, mathematics, physics, or related fields, demonstrating both analytical capability and enthusiasm for computational science. The University of Cambridge's vibrant academic environment, state-of-the-art facilities, and extensive network of alumni and industry contacts make this program an exemplary choice for students aspiring to become leaders in scientific computing and data-driven research.

The taught element comprises core lecture courses on topics of all aspects of scientific computing, and elective lecture courses relevant to the topic of the research project. There is equal examination credit weighting between the taught and the research elements of the course, which is gained by submitting a dissertation on the project and by written assignments and examinations on the core and elective courses, respectively. Weighting of the assessed course components is as follows: Dissertation (research) 50%; written assignments 25%; written examinations 25%.

The core lectures are on topics of high performance scientific computing numerical analysis and advanced numerical methods and techniques. They are organized by the Centre for Scientific Computing and are taught and examined during the first five months (October-February). Their purpose is to provide the students with essential background knowledge for completing their dissertation and for their general education in scientific computing.

In particular, their objective is to introduce students to the simulation science pipeline of problem identification, modelling, simulation and evaluation - all from the perspective of employing high-performance computing (HPC). Numerical discretisation of mathematical models will be a priority, with a specific emphasis on understanding the trade-offs (in terms of modelling time, pre-processing time, computational time, and post-processing time) that must be made when solving realistic science and engineering problems. Understanding and working with computational methods and parallel computing will be a high priority. To help the students understand the material, the lecturers will furnish the courses with practical coursework assignments.

The lectures on topics of numerical analysis and HPC are complemented with hands-on practicals using Linux-based laptops provided by the course (students may bring their own), as well as on the University’s High Performance Computing Service.

Appropriate elective lecture courses are selected from Master’s-level courses offered by the Departments of the School of Physical Sciences, Technology or Biological Sciences. The choice of courses will be such as to provide the students with essential background knowledge for completing their theses and for their general education in the materials science application of the project. They are decided in consultation with the project supervisor. While every effort is made within the Departments to arrange the timetable in a coherent fashion, it is inevitable that some combinations of courses will be ruled out by their schedule, particularly if the choices span more than one department.

Feedback

Feedback on the student’s performance on their examination and on the written assignment results is provided by the course Director; feedback on their research project progress is provided by their research project supervisor. Students receive written termly progress reports.

Graduate students are represented on the Department's Graduate Student Consultative Committee, which normally meets five times a year, and consists of one or more student representatives from each of the research groups. The Committee exists to enable discussion of any issue affecting graduate students and students may approach any member of the Committee to suggest items for discussion.

Assessment

Thesis

The topic of the project (and hence the choice of supervisor) should fall within the research interests of the groups within the Departments of the Schools of Physical Sciences, Technology and Biological Sciences. The project is supervised by a member of the research groups of the Departments of the School. To gain examination credit for the research element, (50% credit towards the degree), students have to submit by the end of August a 15,000-word (maximum) dissertation on a substantial project of original research. The viva voce examination of the dissertation will take place during September, conducted by two examiners (an external examiner from another institution and an internal examiner, who cannot be the student’s supervisor or anyone closely associated with the supervision process) and carried out according to the relevant University regulations. The assessment of the projects is based on the candidate's understanding of the background literature, the commitment of the candidate to the project, the degree of originality shown in the research and the degree of rigour applied in justifying any conclusions.

Essays

The taught element is examined in part by means of two written assignments amounting to 6 credit units. Together with the written examination papers (see the relevant section), the students must accumulate a total of 12 units for examination credit (24 hrs course = 4 units, 16 hrs course = 2.5 units, 12 hrs course = 2 units, 6 hrs course = 1 unit).

Written examination

The taught element is examined in part by means of unseen written examination papers also amounting to 6 credit units. Together with the two written assignments, the students must accumulate a total of 12 units for examination credit (24 hrs course = 4 units, 16 hrs course = 2.5 units, 12 hrs course = 2 units, 6 hrs course = 1 unit).

• Magistr (Master's Degree) at Pass level. Diploma Specialista (completed post-1991) with a minimum overall grade of good or 4/5 Bachelor's from Moscow Institute of Physics and Technology and other prestigious institutions with an overall grade of 4/5 Bologna Bachelor's from other institutions with an overall grade of 5/5, Excellent
• Diploma Specialista (completed post-1991) with a minimum overall grade of Excellent or 5/5 Bachelor's from Moscow Institute of Physics and Technology and other prestigious institutions with an overall grade of 5/5
• IELTS (Academic) 7.0
• TOEFL Internet Score 100
• £50 application fee
• First Academic Reference
• Second Academic Reference
• Transcript
• Personal Reference. 

The University of Cambridge offers a range of financial support options for students enrolled in the Scientific Computing MSc program. Prospective students are encouraged to explore several funding sources to help cover tuition fees and living expenses during their studies. These include university scholarships, studentships, and bursaries specifically designated for postgraduate science and engineering students. The university's funding database provides detailed information on scholarships available for international and UK students, with some awards dedicated to those pursuing computational sciences. Additionally, students may be eligible to apply for external funding from government bodies, research councils, and charitable organizations. The British government offers loan schemes for postgraduate students, which can cover tuition and living costs for eligible applicants. The Cambridge Trusts offer a variety of scholarships and bursaries based on academic merit and financial need, some of which are open to international students. The department itself may also have limited funding opportunities or departmental scholarships that support outstanding applicants. Students are advised to carefully review the eligibility criteria and application procedures for each funding source well in advance of application deadlines. In some cases, teaching or research assistantships may be available, providing students with additional income in exchange for teaching duties or research support. Living expenses in Cambridge can vary, but students are encouraged to budget for accommodation, food, transportation, and other personal costs. For international students, additional costs such as visa fees and health insurance should also be considered. The university offers guidance services to help students identify and apply for suitable financial aid options. Securing funding is an important step in planning for postgraduate studies in Scientific Computing at Cambridge, and early preparation maximizes the chances of receiving financial support.

The MPhil in Scientific Computing at the University of Cambridge is a rigorous postgraduate program designed to equip students with advanced knowledge and practical skills in computational techniques applied to scientific research. This program aims to prepare graduates for careers in academia, industry, and research institutions by providing a deep understanding of algorithms, numerical methods, and software development relevant to scientific problems across various disciplines, including physics, engineering, biology, and environmental science. Students will engage in coursework that covers topics such as high-performance computing, simulation, data analysis, and mathematical modeling, ensuring they can develop and implement computational solutions for complex scientific questions. The program emphasizes a strong foundation in programming languages, including Python, C++, and others, complemented by training in the use of modern computational tools and platforms. Throughout the course, students are encouraged to undertake independent research projects, often in collaboration with academic supervisors and industry partners, fostering a practical understanding of scientific computing applications. The program duration is typically one year and combines lectures, seminars, practical workshops, and project work. Assessment methods include written examinations, coursework, and dissertation submissions. The vibrant academic environment of the University of Cambridge, along with access to world-class laboratories, software resources, and research groups, provides an ideal setting for students to advance their expertise. Graduates of the program often proceed to doctoral research, employment in high-tech industries, or roles in scientific consultancy and data analysis. Admission requirements generally include a strong academic background in mathematics, computer science, or a related field, along with relevant research experience. The program's structure ensures a balanced blend of theoretical rigor and practical application, fostering innovative problem-solving skills essential for tackling modern scientific challenges.