Postdoc: Emotion-Driven Dengue Forecasting

Vrije Universiteit; Amsterdam; In loco; EUR 41.000 - 64.000; Tempo pieno

Vantaggi offerti da questo lavoro

8% holiday allowance

End-of-year bonus

ABP pension

Commuting contributions

Personalized benefits package

Descrizione del lavoro

Institute for Environmental Studies (IVM) at Vrije Universiteit Amsterdam invites applications for an 8-month research position in the FEEL project. You will explore whether emotions expressed in news and public social media can improve dengue forecasts alongside climate information.

Based in Amsterdam, you’ll connect human behaviour, climate and health through data and modeling. You will develop and evaluate methods to extract emotion indicators, build forecasting models, and prepare

Competenze

• PhD in climate science, environmental science, physics or a related quantitative discipline.
• Experience with statistical modelling of climate impacts, health outcomes or social vulnerability.
• Strong programming skills in Python or R; experience with spatial and temporal data.

Mansioni

• Develop and evaluate methods to derive indicators of human emotions from news and social media.
• Build and validate forecasting models to test emotion indicators vs. climate data and past dengue incidence.
• Assess usefulness of indicators across locations and lead times.
• Prepare a scientific manuscript and document methods and code for reproducible research.

Conoscenze

PhD in climate science / environmental

Python or R

statistical modelling

spatial data analysis

Formazione

PhD in a quantitative discipline

Descrizione del lavoro

Institute for Environmental Studies (IVM) at Vrije Universiteit Amsterdam invites applications for an 8-month research position in the FEEL project. You will explore whether emotions expressed in news and public social media can improve dengue forecasts alongside climate information.

Based in Amsterdam, you’ll connect human behaviour, climate and health through data and modeling. You will develop and evaluate methods to extract emotion indicators, build forecasting models, and prepare