PhD position in geo-computational exposure assessment modeling

PhD student: Developing a model of geo-computational exposure assessment for studying health effects of urban environmental risk factors in Exposome-NL (1.0 FTE)

Deadline: 30 October 2020

The environment we live in has a significant impact on our health, explaining an estimated 70% of the non-communicable chronic disease burden. A large number of environmental stressors may jointly affect our health. Leading scientists in Europe and the USA have formalized this perspective as the Exposome concept: the sum of all non-genetic drivers of health and diseases. Interacting with the genome, it defines individual health at different stages throughout the life course.

There is a need to scale up and generalize the different ways how the general external Exposome can be measured in terms of diverse (geo)data sources and (geo-)computational methods. Environmental factors have been modelled in different forms, including spatial fields (continuous value surfaces), spatial objects (discrete spatially bounded entities), events (spatially and temporally bounded entities) and networks (quantified relations between objects). In the past, approaches for different environmental stressors have been less systematic, ranging from simplistic point-in-polygon tests and buffer based approaches to network-based interaction measures on different computational platforms.

The ambition of this project is to generalize computational models over different sorts of data, spatial information concepts (field, object, event and network) and computing infrastructures (desktop-based, database, cloud-based, and distributed). This includes developing semantic models of the different sorts of environmental factors, data sources and problem sizes, as well as semantic models of the different geo-computational transformations needed for assessing exposure. It also includes an investigation into scalable computational infrastructures (such as parallelization and cloud computing) and prototypical implementations for deploying exposure models for large problem sizes.

We are looking for a colleague who has a completed Master’s degree in computational geoscience, data science, GIScience, computer science, or a related discipline, with a strong interest in environment and health. We prefer colleagues with experience in conceptual modeling of geographic information and GIS workflows. You will work in a multi-center study funded by the prestigious Gravitation programme of the Dutch research funding organization NWO. Exposome-NL is a 10-year research programme of multiple Dutch universities.

Best regards,

Simon Scheider

Human Geography and Spatial Planning
Universiteit Utrecht

Vening Meineszgebouw A
Room 6.16
Princetonlaan 8a
The Netherlands

+31 30 253 2966