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<div class="">The <a href="http://www.lancaster.ac.uk/dsne" class="">Data Science of the Natural Environment</a> (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. </div>
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<div class="">Associated with DSNE, we are advertising <b class="">several PhD positions across environmental data science</b>, 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. </div>
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<div class="">Detailed information on the advertised PhD projects can be found <a href="https://www.dropbox.com/s/95mp9q9rh1h8dho/DSNE_PhD_advertisement.pdf?dl=0" class="">here</a>, including information on the application process. Titles of the projects are
listed at the end of this message.</div>
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<div class=""><b class="">Application deadline</b>: <font color="#ff2600" class="">
5pm (GMT), 11th February 2019</font> (<b class=""><i class="">extended by popular demand!</i></b>)</div>
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<div class=""><b class="">General enquiries</b>: <a href="mailto:dsne@lancaster.ac.uk" class="">dsne@lancaster.ac.uk</a></div>
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<div class="">Unfortunately, the PhD funding is only for UK and EU applicants. </div>
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<div class=""><b class="">Available PhD project titles</b></div>
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<li class="">Data science approaches to projecting future global-to-local air quality and climate</li></ul>
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<li class="">Robust assessment of change points in air quality looking across scales and across multiple data sources</li><div class=""><br class="">
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<li class="">Decision making in the face of uncertainty: A qualitative study</li><div class=""><br class="">
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<li class="">Understanding trust in environmental data science: Cross disciplines and cross cultures</li></ul>
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<li class="">Non-parametric mixture methods for improved satellite altimeter retrievals over ice sheets</li></ul>
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<li class="">Diagnosing Antarctic ice shelf risk using coupled computational modelling</li></ul>
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<li class="">Automated quantification of Greenland ice sheet melting using spaceborne radar data and multivariate changepoint methods</li></ul>
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<li class="">Downscaling and cross-scale integration of land use data and models for building pathways towards sustainable food and land use systems</li></ul>
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<li class="">Integrating agent-based land use models and macro models for improved environmental decision-making</li></ul>
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<li class="">Mapping the rates of changes in land physical properties using remote sensing data</li></ul>
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<li class="">Statistical modelling and physical drivers of extreme hydrological events</li></ul>
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