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The CDT programme is composed of a one-year Master's course followed by a three-year PhD.

The first year of the CDT is a materials modelling option within the MPhil in Scientific Computing at the University of Cambridge and a range of additional training elements.

The main aim of the first year is to provide education in Computational Methods for Materials Science of the highest quality at a graduate level, and so produce graduates of the calibre suitable for entering our PhD programme, who will then be highly coveted by industry, the professions, and the public service. It will also provide training for the academic researchers and teachers of the future who will pursue research of the highest quality in Computational Methods for Materials Science.

The MPhil is administered by the Department of Physics, but it serves the training needs of the Schools of Physical Sciences, Technology and Biological Sciences. The ability to have a single Master’s course for such a broad range of disciplines and applications is achieved by offering core (i.e. common for all students) numerical and High Performance Computing (HPC) lecture courses, and complementing them with elective courses relevant to the specific discipline applications.

In this way, it is possible to generate a bespoke training portfolio for each student without losing the benefits of a cohort training approach. This bespoke course is fully flexible in allowing each student to liaise with their academic or industrial supervisor to choose a study area of mutual interest.

An indication of the success of the MPhil in Scientific Computing is that all of its past graduates to-date have been offered fully-funded PhD placements in this University and elsewhere.

In addition to the comprehensive set of Masters-level courses provided by the MPhil (and other courses from across the University in fields that are relevant to the PhD topic), it will also be possible for students to take supplementary courses (not for examination) at undergraduate level where a specific need is identified, in order to ensure that any prerequisite knowledge for the Masters courses is in place.

Moreover, depending on their background and circumstances, students may be offered places in the HPC Autumn Academy, which takes place just before the start of the academic year (two weeks in September).

By the end of the course, students will have:

  • a comprehensive understanding of numerical methods, computational approaches for applying them efficiently, and a thorough knowledge of the literature that is relevant to their own research;
  • demonstrated originality in the application of knowledge, together with a practical understanding of how research and enquiry are used to create and interpret knowledge in their field;
  • shown abilities in the critical evaluation of current research and research techniques and methodologies;
  • demonstrated self-direction and originality in tackling and solving problems, and acted autonomously in the planning and implementation of research.

The final three years consist of a PhD research project, with student-led choice of projects from a large pool of options that are supplied by leading University researchers associated with the CDT. However, there is a check point at the end of the MPhil to determine whether students should continue on for PhD studies. In line with the arrangements of our existing programme, if the student gains a Distinction (i.e. an average grade greater than 75%), admission to the PhD programme is guaranteed. If, however, they are graded between 60% and 74%, their case will be reviewed by the CDT Academic Committee on a case-by-case basis.

Based on their first year training and the outcome of examinations, the students will then choose a research project to continue for their PhD from the start of the second year. This project may be based on the same topic as the preceding placement/project, or it may be different.

At the end of their second year (the end of the first year of the PhD programme), students must submit a report describing their progress, which is examined by two academics not directly associated with the project, who make a recommendation to the Board of Graduate Studies about whether the student should be allowed to continue with the PhD. The department hosting your PhD will provide details of the form of the report.

At the end of the third year (the end of the second year of the PhD programme), participating Departments in the CDT usually impose a requirement for students to present their research, either orally or by making a poster. Students will be encouraged to use these as a basis for making presentations at international conferences, such as one of the Materials Research Society or American Physical/Chemical Society meetings.

Cohort Training Approach

There is a compelling industrial demand to address a shortage of skilled graduates with a comprehensive understanding of the fundamental principles of mathematical and computational modelling. This was recently emphasised in a 2016 report on the “Economic Impact of Materials Modelling”,[1] which revealed a 35% increase in job creation and an eightfold return-on-investment in materials modelling over the range of companies surveyed. Further evidence for the impact of modelling comes from the UK 2014 REF exercise, which included 15 case studies based on materials modelling [2]. The majority of these utilised modelling techniques across a wide range of time and length scales.
This need to bring researchers from diverse backgrounds together can only be addressed by a cohort-based training programme (such as the one delivered through this CDT) because the students require a combination of rigorous training in scientific programming, the fundamentals of numerical analysis and a broad suite of technical courses covering data science techniques. By definition, the type of multiscale modelling of interest to industry involves working collaboratively across traditional subject boundaries which is naturally well suited to the CDT model.
The benefits of cohort training can be summarised as follows. For the student, there is an opportunity to build a peer group across different subject disciplines that will persist far beyond the duration of their initial training, which in turn will help them to develop their own identity as a researcher. There are also chances for networking with peers, subject experts and external visitors - all attracted by the high concentration of excellence in the CMMS CDT. The collaborative nature of the training programme encourages diverse approaches to problem-solving, including the exploration of problems in great detail, as well as enabling independent thinking and engaged learning. For the cohort as a whole, there will be opportunities for the creation of a group identity and learning through teaching (through both peers and those external to the CDT).

More information is available here.
[1] Goldbeck, G., & Court, C. (2016). The Economic Impact of Materials Modelling. Zenodo.
[2] (last accessed 12/2/2018)

Cohort Building Activities

Cohort building activities are organised throughout the duration of the 4-year CDT programme. These activities include welcome meetings, social events, group seminars, team projects and competitions, and a number of continuous professional development events. Amongst these are industrial talks and seminars, an internship programme, lectures on intellectual property, entrepreneurship, consultancy, technology transfer, commercialisation of research work and on responsible research and innovation.