Institution (Location)
Amsterdam Public Health research institute
Start Date
October 15, 2019
Position Description

Within the Geoscience and health cohort consortium ( we are looking for an ambitious PhD candidate (Data Scientist) in Applied Machine Learning in Environmental Epidemiology. Obesity and cardiovascular diseases (CVD) are complex, systemic, multi-causal problems. Environmental exposures form important but underappreciated risk factors contributing to the development of CVD. Environments can impact on CVD indirectly, e.g. by an abundant availability of foods and lack of options to be active, which forms barriers to adhere to a healthy lifestyle, and directly e.g. via ambient air pollution. Addressing these environmental ‘causes of the causes’ by increasing opportunities for healthier diets, facilitating physical activity and reducing air pollution are key priorities to decrease CVD risk. To do so it is essential to improve our knowledge on how built-and social environmental characteristics interact with each other and with individual-level characteristics, and how they are related to behaviour and health for various population groups. Abundant and high-quality data are required and are now becoming available as well as novel analytical approaches to study this better. Within the GEohealth Cohort COnsortium (see for more information), 20 renowned, longitudinal Dutch cohorts are currently being enriched with a wide variety of environmental (GIS) data. This will result in a huge set of exposure measures linked to thousands of individual-level behaviour and health profiles over multiple time-points. Responsibilities include the development and application of state-of-the-art statistics and machine learning applications such as decision trees and clustering algorithms to identify environmental factors that are related to lifestyle behaviours, obesity and risk of cardiovascular disease. This ambitious research project will be completed with a PhD-thesis and will be executed in close collaboration with a multidisciplinary team of epidemilogists, biostatisticians, geographers and health researchers.

Candidate’s Qualifications (who should apply)

Your profile: a master’s or equivalent degree in a relevant field (Computer Science with specialisation in data mining, machine learning and/or deep learning); a strong interest in data science research with a particular focus on environmental epidemiology or cardiovascular disease; working knowledge on relevant programming languages (Python, R, Bash, etc.); affinity with translational research, analytical validation and complex systems modelling; a pro-active, organised, creative, and ambitious attitude. Experience in environmental and/or health epidemiology is a strength. Especially candidates with an interest in crossing disciplinary boundaries are encouraged to apply. The candidate should be able to work independently and have good verbal and written communication skills in English.

How to Apply

More information and application via this link:


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