We are pleased to announce an opportunity for a 3-year PhD studentship in epidemiology and public health with focus on research methods within the School of Public Health at Imperial College London, funded by the London Interdisciplinary Social Sciences Doctoral Training Partnership (LISS DTP).

Start date, research environment and supervisors

This project will start on October 1st 2022 in the School of Public Health's Department of Epidemiology and Biostatistics, with Betatechnology Ltd Partnership. The primary supervisor is Professor Marjo-Riitta Jarvelin. The student will be co-supervised by Dr Georgina Hosang (Queen Mary University London) and Dr Evangelia Tzala (Imperial College London). The student will work under the theme of life-course epidemiology. The project is offered as a +3 PhD. The specific route will depend on the student's previous training, in particular if they meet the ESRC's core research methods training requirements.

Our researchers are uniquely placed to lead collaborative projects nationally and internationally, producing high quality research  and working in collaboration with academic and non-academic partners, and health agencies to translate the knowledge into policy and improved population health.

A key focus of our mission and strategy is to train and develop the next generation of academic leaders in the field of social sciences and health research. Successful PhD candidates will be supported by a leading bespoke training programme to ensure they are equipped with the appropriate skills and experience to become first class researchers.

Duration and funding

LISS-DTP ESRC Collaborative Studentship for 3-year PhD project (candidate should have a relevant Master’s degree) includes Home Studentship, fees and stipend (please note that international/non-UK student fees are not covered by this funding). The annual stipend will be approximately £17,600 tax-free.

Project summary

Dynamic interpersonal, biological, psychological and behavioural systems interact with broader contextual factors (“micro/macro-systems”, e.g. family’s social circumstances and functioning, wealth, poverty, health care, work environments), to shape health-related ageing and wellbeing over the life span. We seek to unravel pathways through which psychosocial factors impact the ageing process through the lifcourse. We will approach health as a dynamic system and capture fluctuation over time from prenatal period until middle age. We hypothesize that socioeconomic distress and psychosocial adversities (e.g. poverty, low education, mental distress) may be both risk factors for, and consequences of, metabolic adversities such as obesity that promote risk of developing long-term health conditions including adult-onset diabetes (type 2 diabetes, T2D). This is creating a vicious cycle of ill-health that affects people’s wellbeing, quality of life and can lead to accelerated ageing process and premature mortality. The importance of bio-psychosocial model, in theory and practice, will be investigated to promote healthy ageing. Our project will be embedded in social science conceptual framework, its theories will be reviewed during the studies.

Ageing represents the accumulation of changes in a human being over lifespan and can encompass physical, psychological, behavioural and social aspects. The mechanisms of ageing process are not well understood but are assigned to the damage in the body that may lead biological systems to fail. In clinical medicine and social science context ageing can be defined and assessed in multiple ways e.g. by cognition, perception of health and wellbeing and disease development.

Ageing will be assessed broadly and will not only include ageing associated diseases and their intermediate markers (e.g. T2D, glucose levels, obesity) but also physical and cognitive function as an indicator of mental deterioration, subjective wellbeing and mental health (particularly depression), quality of life (QoL) indicators, life- satisfaction, socioeconomic circumstances and participation in the work force. The novel Bayesian analytical approach (and software) developed in house embedded in dynamic systems, allows integration of hundreds – thousands of factors across the life-course. Depending on the context the same factor maybe an outcome or it can be an exposure in the pathway analysis. In this project, we will restrict disease exploration on conditions essential for public health, i.e. T2D and related factors (glucose, body mass index) known to contribute to or mimic the ageing process. T2D that is exponentially increasing in the populations is a good “model disease” in this context. High glucose levels, even without T2D, may contribute to vascular damage and lead to dementia.

A comprehensive training plan will be developed for the successful student. This will include the opportunity of an internship at expert partner (Beta Technology LTD) for the dissemination, impact driven communication and exploitation of the scientific findings of this project.

Eligibility

Applicants must either; be a UK National (meeting residency requirements), have settled status, have pre-settled status (meeting residency requirements), or have indefinite leave to remain or enter; or be able to self-fund the additional amount required to cover international fees for a PhD (i.e. additional £ 34,300). Please find the definition of international students for fees purposes here.

Students applying for CASE studentships must meet the ESRC eligibility guidelines in terms of UK residency status and academic qualifications, specifically core social science research methods training that must already have been undertaken for +3 awards. Please see the LISS-DTP website for further details.

Essential criteria

  • Applicants should have a good undergraduate degree (2:1 or first class) in biosciences, social medicine, social/medical biostatistics  or a related field. When applying for the +3 studentship, the student should have a Master’s degree (Merit or above) in relevant area and training in quantitative research methods.  Additional training in qualitative research is an advantage.
  • Training and/or an interest in biostatistics, epidemiology and longitudinal data modelling.
  • Excellent communication skills and an ability to work independently and as part of a multidisciplinary international team.

Desirable criteria

  • Experience with working in large scale datasets.
  • Experience with methodological software (e.g. R, Matlab)
  • Public health or medical training.

Funding

Funding will cover the cost of UK home tuition fees plus a stipend of £17,609 per annum. Other funds are also available for research costs.

Application

The application form can be downloaded here.

For further information please do not hesitate to contact the academic leads for this project,  Dr Evangelia Tzala or Professor Marjo-Riitta Jarvelin, to discuss details of the project or the postgraduate administrator Anja Gizdavcic for information about the application process. Interested candidates should apply by emailing the documents to Dr Tzala and sph-pgradmin@ic.ac.uk.

Incomplete applications will not be considered.

Applications should include the following:

  1. An up-to-date CV
  2. A personal statement including why you are interested in undertaking the project, what relevant skills, training and knowledge you would bring to the project; and any ideas you have for executing the named project (max 1 page)
  3. A completed application form. Download above.
  4. Academic transcripts
  5. Contact details for 2 referees, at least one of which should be a recent academic referee.

We strongly encourage applications from the UK students from ethnic minority and underrepresented groups in biosocial sciences.

The closing date for applications is Monday 28th February 2022.

Shortlisted candidates will be contacted to arrange interviews, which will take place in the week commencing March 14, 2022. Due to the high number of applications, we will not be able to respond to individual candidates.

The successful candidate will be asked to complete an Advanced DBS check upon commencing the post.