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Closed for application
C39.CU2.29

Using Machine Learning for climate-related environmental and socio-economic vulnerability

  • Reference person
    Margherita
    Righini
    margherita.righini@iusspavia.it
  • Host University/Institute
    IUSS Pavia
  • Internship
    Y
  • Research Keywords
    Copernicus
    Common Agricoltural Policy
    Space Economy
  • Reference ERCs
    PE10_4 Terrestrial ecology, land cover change
    PE10_13 Physical geography, geomorphology
    PE10_14 Earth observations from space/remote sensing
  • Reference SDGs
    GOAL 9: Industry Innovation and Infrastructure
    0
    GOAL 15: Life on Land
  • Co-Supervisor

Description

Climate change has wide-ranging impacts on ecosystems, economic sectors and human well-being. The effectiveness of climate-related risk assessment depends on the spatial and temporal accuracy of the information to offer more up-to-date modelling tools to produce reliable vulnerability analysis. The main objective of the research is to develop a new approach to define the climate environmental and socio-economic vulnerability in coastal and/or urban areas through the use of Machine Learning techniques and multi-source data, including Earth Observation data. The PhD should:(1) Develop new vulnerability models through Machine Learning techniques;(2) Combine indicators across the three components of exposure, sensitivity, and adaptive capacity, and construct or improve vulnerability indices;(3) Assess the ECV land cover change and its environmental and socio-economic impacts in terms of ecosystem services degradation; (4) Develop a decision support system (DSS) to build a sustainable management strategy for resilience to climate change.The research is aligned with the objectives of the PNRR, contributing to improve the advanced and integrated forecasting system to counter anthropogenic and climate change impacts and to support ecosystem services by leveraging the most advanced modelling techniques, data and analytical processing solutions, to identify possible risks and their impacts at an early stage developing new tools to support decision making.

Suggested skills:

-Knowledge of artificial intelligence approaches (e.g., fuzzy logic, Bayesian systems) applied to determine the response of ecosystems to climate change;- Experience in the implementation of integrated decision support systems for the innovative tool in coastal, agroforestry and urban domain;-Experience in using the European Earth Observation Program (Copernicus).

Research team and environment

IUSS mission is to provide advanced education to undergraduate and graduate students, as well as fundamental and applied research in the fields of Science, Technology, Engineering and Mathematics (STEM), and Human, Social and Life Sciences. At IUSS, PhD candidates will find an open multidisciplinary environment offering real opportunities for developing academic and professional tools for facing the challenges arising from increasing complexity and fast changes in the society and the environment. IUSS is always and actively committed towards internationalisation, inclusion and diversity. The selected candidate will join the research centre on Climate change impAct studies for RISk MAnagement (CARISMA). The CARISMA team is composed by STEM and Social scientists working in the prism of climate change on data analysis including Copenricus and modelling of Earth System and economic system processes; impact assessment of extreme natural events and anthropogenic activities on human and natural environments; risk assessment and management of natural and anthropogenic hazards; formulation and proposal of new economic, political and legal models of sustainable development. The research activity will be carried out in collaboration with the Space Unit and Data Unit of the Italian Institute for Environmental Protection and Research (ISPRA) and may include stays at the ISPRA Research Centre (Rome).