Research Associate in Population Health Modelling and Data Science (University of Sheffield)
We are seeking to appoint a Research Associate to engage in world-leading research in population health modelling and data science, with a specific focus on developing a smoking cessation model for England in partnership with University College London. The role sits within the Cancer Research UK funded programme grant “Targeting multiple levels of the smoking cessation system using novel scientific approaches”.
The role involves developing a novel agent-based model (ABM) of smoking cessation behaviours and health outcomes. You will use the ontology of the social psychology COM-B (capability-opportunity-motivation-behaviour) framework to inform the conceptual and detailed design of the model. You will calibrate the model using empirical data for England and use it to analyse the effectiveness of interventions to promote smoking cessation. Following open science principles, you will develop the ABM as open-source software and produce an online tutorial to support potential users.
The role requires a keen enthusiasm to work across disciplinary boundaries in developing world-class and transformative research in the modelling of social behaviours. During the project you will be able to draw on world-leading expertise in health behaviours, epidemiology, health economics, agent-based modelling, and machine learning.
The post will be based in the Complex Optimization and Decision Making Laboratory, led by Professor Robin Purshouse, within the Department of Automatic Control and Systems Engineering (ACSE) with support from Professor Alan Brennan in the School of Health and Related Research (ScHARR). The post is integral to ACSE and ScHARR strategies to develop complex systems modelling platforms to support societal grand challenges for improving wellbeing and reducing health inequalities. The new ABM will leverage ScHARR’s existing Sheffield Tobacco Policy Model and related findings.
This is a companion discussion topic for the original entry at https://www.comses.net/jobs/565/