PhD studentship in data science in epidemiology: Female body temperature - data mining approaches to predicting ovulation and fertility problems

Many couples have difficulty conceiving, both female and male fertility problems are surprisingly common. For example, one in ten women of child bearing age suffer from a condition known as polycystic ovary syndrome (PCOS) which leads to irregular periods and other symptoms which result in reduced fertility. A key to successful conception if there is reduced fertility, or even if you are happily, normally fertile is the ability to track the menstrual cycle and the timing of ovulation. As part of this, the gold standard for identification of ovulation has been intra-vaginal ultrasound, however, a new method has been identified that has been shown to be as accurate but is much more convenient and has also already been shown to be an aid to the diagnoses of other underlying fertility problems.
A highly accurate temperature sensor is worn internally overnight and records temperature every five minutes. The data are stored within the device which, over a month, routinely downloads via a smartphone app to a central server for data processing and anonymous storage. The individual measurements are used to provide a representative body temperature for every day of the female cycle and these day-summary measurements are then used to predict and also to confirm ovulation for the user. Different patterns of change in the data can also be associated with a range of fertility problems/disease. These cycle monitoring products are commercially available and we have access to a large and growing database of anonymised overnight and day-summary data.
Objectives
1. To initially identify different profiles of change within overnight/monthly temperature cycles.
2. To investigate associations between profiles of temperature change and specific fertility problems/disease.
3. To improve prediction and verification of the timing of ovulation from overnight/monthly temperature data.
The Studentship provides a valuable opportunity to gain skills and training in working with ‘big data’ at an applied level. The successful candidate will develop skills in epidemiology and the rapidly developing field of data science, specifically learning state-of-the-art statistical approaches to investigating functional data, data mining and epidemiology, as well as techniques to process large datasets and present / visualise findings in accessible formats. The student will also attend relevant internal and external training courses.
Application Details
Applicants for this 3-year PhD Studentship should have a strong background and interest in the processing and analysis of expansive data sets, statistical modelling, data mining, or related disciplines, with demonstrable experience in relevant statistical software and basic statistical programming (e.g. R / Python / MATLAB). The student will be co-hosted in the MRC Integrative Epidemiology Unit in the School of Social and Community Medicine, which has a strong reputation in medical statistics and is at the forefront of the application of state-of-the-art statistical and data mining techniques in epidemiology.
Applications are invited from graduates with a First or Upper Second Class Bachelor’s degree (in either a statistical / computer science or a relevant health services research related subject including substantial quantitative training). A Master’s degree in epidemiology or a related discipline or equivalent research experience would be advantageous.
The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study and day to day living costs can be found by visiting http://www.bristol.ac.uk/study/postgraduate/fees-and-funding/ . There is NO funding attached to this project, the successful applicant will need to be entirely self-funded.
For non-native English Speakers, a minimum score of 6.5 at IELTS is required, minimum 6.0 in all bands or a Trintiy Integrated Skills in English (ISE) pass in all elements, with a merit in at least one.
Informal enquiries should be directed to Dr Tom Gaunt ([Email Address Removed]) or Prof Toby Knowles ([Email Address Removed]).

When applying please select ’Veterinary Science PhD’ from the Faculty of Health Sciences.