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PhD studentship, Predictive models in health and morbidity, Heriot-Watt U
martes, 08 de octubre de 2019

Predictive models in health and morbidity trends related to insurance

Applications are invited for a 3 year PhD studentship at the School of Mathematical and Computer Sciences, Heriot-Watt University, to start on 1st December 2019 or as soon as possible after that. We are looking for an enthusiastic and highly-motivated student to join a vibrant research group working at the interface of statistical modelling and actuarial science.

The deadline for applications is 23 October 2019.


The project will be mainly funded by the Society of Actuaries under a Centre of Actuarial Excellence Research Grant. The scholarship consists of full tuition fees for an Overseas or Home/EU student and will also provide a stipend similar to UKRI level.

Entry requirements

The successful candidate should have (or expected to achieve) an undergraduate degree with an excellent or very good classification (1st class or high 2:1, or equivalent) and a Masterʼs degree in statistics, actuarial science or related discipline, preferably at distinction level. Experience in computing using R and strong IT skills will be highly beneficial.

Project description

Over recent decades, medical interventions and advances have become important drivers of health and morbidity trends. In this project we will develop, evaluate and assess statistical predictive models for morbidity risk and underlying trends, relating to particular major conditions such as heart disease, diabetes and cancer, using data from major USA- and UK-based sources. The principal aim will be to address the timely need to develop robust predictive models for rapidly changing morbidity risks and relevant impact on health-related insurance. A Bayesian approach will be employed, to allow for uncertainty quantification, under which model diagnostics, assessment and selection will be considered for a range of models including machine learning-based approaches. The application of such methodology in the context of this project is novel and at the forefront of current practice.

Further details about the hosting institution can be found at:

For information about how to apply, please visit

To discuss further details about the project please contact Professor G Streftaris ( Esta dirección de correo electrónico está protegida contra los robots de spam, necesita tener Javascript activado para poder verla ).