Training and development
The principal supervisor will be responsible for discussing training needs with the student at regular intervals throughout the project and drawing up a training plan together with the student which ensures that they complete the 70 hours per annum transferable skills training recommended by QMUL's training guide, and as part of the QMUL skills-based points system (Researcher Development Department 2018). The principal supervisor will also take responsibility for identifying suitable training. Training will include the induction programme run by the School of Medicine and Dentistry, informal training within CPCPH and formal training inside and outside the School.
QMUL has a strong reputation for innovation through inter-disciplinary and cross-faculty collaboration. The Research Excellence Framework 2014 rated QMUL 5th in the UK for "world-leading or internationally excellent" research outputs, and HEFCE 2013 noted that QMUL has the highest rate for timely PhD completion of any UK university.
The CPCPH has expertise in qualitative research methods which are taught in seminars and as part of bachelor- and masters-level courses (School of Medicine and Dentistry 2018). The student could attend modules from these courses to re-familiarise themselves with research approaches and gain new knowledge. This will prepare the student for mixed-methods approaches and for qualitative data analysis including thematic analysis.
Statistical training will be given on the Research methods course but where necessary we will provide specialist training with one or two of our own statisticians from the centre for health sciences. Additionally the Royal Statistical Society run a variety of training courses throughout the year that may be of use.
Systematic narrative and critical literature reviewing
Within CPCPH we have considerable expertise in different aspects of conducting reviews of methodology, including search strategies, data extraction, issues with identifying methodology from papers, and presenting results. The student will receive appropriate training from CPCPH staff and if staff are unavailable we will organise attendance on one of the many courses available in this field.
Machine learning and computational training
The Faculty of Science and Engineering provides expertise and teaching in machine learning and related fields, and The Blizard Institute, where the CPCPH is based, has relationships with this faculty through the programmes including Queen Mary Innovation and the "CANBUILD - deconstructing cancer" project. The student will be able to access modules relating to the computational element of this PhD, contributing to the existing reputation the Blizard Institute has for interdisciplinary research.
The university runs excellent courses for the software applications they will need to be proficient in, for example Endnote, Microsoft Word and PowerPoint. We can also include the PhD student in our M.Sc. for Global Health and Innovation modules such as: introductions to the library and searching electronic data bases. Additionally, there are excellent short course in using statistical software packages. This will also help foster a working team spirit within the CPCPH.