PRR Project

Summary

As demonstrated by the COVID-19 pandemics, there is an emergence to promote knowledge acquisition and tools to inform the climate and environmental policies supported by an in-depth knowledge on the Earth´s natural systems and human health interactions. Such interactions can be explored through innovative analytic, quantitative, and predictive modelling tools capable of integrating multimodal data in multi-scale spatiotemporal dimensions.Earth Observation (EO) data from satellites provide a wide range of relevant information entailing global (Earth) and local (e.g. municipalities) meanings. EO data can provide information about air pollution, heat waves, urban green and blue areas, vegetation, floods, and droughts, just to name a few. EO data have been key to better understanding climate and environmental changes and their impacts on populations. When satellite imagery data are combined with in-situ data (e.g., ground-based meteorology stations), as well as available health data, it is possible to develop and apply spatial data science modelling and machine learning (ML) algorithms for analysis and prediction of environmental and health impacts of climate events. The knowledge acquired within the environmental-health modelling system provides actionable insights that should lead to responsive actions through preventive measures to mitigate adverse outcomes. The continuous feedback from the environmental-health system leverages and refines the system´s predictive models, creating a dynamic and adaptive tool which can be used to better inform policymaking for addressing critical societal challenges.Spatial data science (SDS) is an emerging scientific field concerned with application of data science methods to spatial data. This set of methodological tools provide innovative solutions to address problems that impact our health and our planet (i.e., environmental-health system). The development of SDS is based on the increase in computational power to process near-real-time large amounts of spatial data and the efforts done internationally by the scientific community to share environmental and health open data while promoting better science. This scientific area is fully aligned with CERENA’s Environment group strategy for the next five years and contributes to IST-ID goals related to the Sustainable Development Goals. Besides, it promotes the participation of both institutions (IST-ID and CERENA) in the definition of more efficient public policies through the deployment of monitoring and predictive tools powered by state-of-the-art spatiotemporal modelling techniques using EO data. CERENA has a strong background on processing and modelling EO data for environmental and Earth modelling at different scales and on spatiotemporal modelling of diseases and environmental-health interactions in mainland Portugal.This position requires the candidate to develop research and coordination activities related to grand challenges in the use of EO with SDS to model and predict health-environment interactions. These activities include the design, development, and implementation of SDS and ML modelling algorithms to environmental health, for aiding in disaster risk reduction, sustainable urban and coastal management, air quality improvement, and climate adaptation and mitigation strategies.The researcher should have a strong track-record demonstrated by high-impact publications and leading of projects in applied SDS and geomatics (i.e., scientific programming) specifically demonstrating her/his ability to push forward the current boundaries on SDS modelling and ML algorithms to develop and implement environmental and epidemiological modeling and predictive frameworks, develop advanced tools for environmental monitoring and information visualization. The researcher must demonstrate solid scientific skills and commitment in building new networks with other researchers in fields addressing knowledge gaps in environmental-health and build her/his own group by recruiting young talent from graduate and post-graduate degrees. The researcher will also be responsible to prepare proposals for national and international calls and networking with other researchers, group leaders and non-academic entities in environmental-health fields.This position will consolidate, with a permanent contract, the high-impact work currently developed on this scientific field within CERENA. During the COVID-19 pandemics, CERENA developed quantitative methods to model the spatiotemporal evolution of the disease in mainland Portugal (projects SMOCK and SCOPE, both funded by the Portuguese Foundation for Science and Technology). These tools were adopted by the national health authorities to inform and support decision-making in monitoring and evaluating spread dynamics. The researcher will lead this effort to consolidate the ongoing collaborations with the Portuguese Directorate-General for Health and the National Institute of Health Doutor Ricardo Jorge.