January 2019
Message 103

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[Met-jobs] Several PhD positions in Data Science of the Natural Environment | Lancaster University, UK | UK/EU funding only

From "Young, Paul" <>
To "" <>
Date Fri, 25 Jan 2019 15:00:35 +0000

The Data Science of the Natural Environment (DSNE) project is a 4-year, £2.6M interdisciplinary research programme that brings together environmental scientists, computer scientists, data scientists and statisticians from Lancaster University and the Centre for Ecology and Hydrology. 

Associated with DSNE, we are advertising several PhD positions across environmental data science, including those that are environment-led, methods-led and social science-led. It is expected that we will fund 5 PhD positions from the range of projects that we are advertising.   

Detailed information on the advertised PhD projects can be found here, including information on the application process. Titles of the projects are listed at the end of this message.

Application deadline5pm (GMT), 11th February 2019 (extended by popular demand!)


Unfortunately, the PhD funding is only for UK and EU applicants. 

Available PhD project titles
  • Data science approaches to projecting future global-to-local air quality and climate
  • Robust assessment of change points in air quality looking across scales and across multiple data sources

  • Decision making in the face of uncertainty: A qualitative study

  • Understanding trust in environmental data science: Cross disciplines and cross cultures
  • Non-parametric mixture methods for improved satellite altimeter retrievals over ice sheets
  • Diagnosing Antarctic ice shelf risk using coupled computational modelling
  • Automated quantification of Greenland ice sheet melting using spaceborne radar data and multivariate changepoint methods
  • Downscaling and cross-scale integration of land use data and models for building pathways towards sustainable food and land use systems
  • Integrating agent-based land use models and macro models for improved environmental decision-making
  • Mapping the rates of changes in land physical properties using remote sensing data
  • Statistical modelling and physical drivers of extreme hydrological events

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