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

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 deadline: 5pm (GMT), 11th February 2019 (extended by popular demand!)

General enquiries: dsne@lancaster.ac.uk

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

Comments are closed.