Applications are invited for a fully-funded PhD studentship in which you will learn to develop cutting-edge data science approaches to address a key environmental science challenge related to sustainable land use.
The studentship is part of the £2.6million EPSRC-funded grant Data Science for the Natural Environment (DSNE), a joint project between Lancaster University and the UK Centre for Ecology & Hydrology. This is an exciting opportunity to work at the heart of a multi-disciplinary team of researchers consisting of environmental scientists, computer scientists, statisticians and stakeholder organisations, working together to deliver methodological innovation in data science to tackle grand challenges around environmental change. The student will be registered at Lancaster University. The studentship covers the full fees and stipend of UK/EU applicants only (i.e. does not cover the full fees of non-EU applicants).
The DSNE research programme is a prestigious and high profile research programme targeting a paradigm shift in the role of data in environmental science and leading to long-term impact in decision making. The research is arranged around methodological developments in three core methodological themes (integrated statistical modelling, machine learning and decision-making, and virtual lab development), interlocked with three challenge themes from the environmental sciences (ice sheet melt prediction, air quality modelling and land-use modelling) The PhD topic available is listed below.
Prospective applicants are encouraged to contact Prof. Pete Atkinson (email@example.com) or Prof. Paula Harrison (email: PaulaHarrison@ceh.ac.uk) before making an application.
To apply, please send a letter of application to firstname.lastname@example.org by 5pm Wednesday 18th March. The letter should include:
- An explanation and resoning about why you want to be considerd for the project
- An explanation of why your skill set and previous education will allow you to be successful in this project (a transcript of your undergraduate or masters degree programme is likely to be helpful)
Unfortunately, while we can cover the full fees and stipend for UK/EU applicants, the full fee of non-EU applicants cannot be covered.
Closing date: Wednesday 18th March
Expected interview date: Wednesday 1st April
Downscaling and cross-scale integration of land use data and models for building pathways towards sustainable food and land use systems
Supervisors: Pete Atkinson, Paula Harrison and Pete Henrys
Food and land use systems are unsustainable in every part of the world. Today’s practices drive biodiversity, forest, and other ecosystem losses; cause water scarcity; and threaten the health of freshwater ecosystems through chemical and fertilizer run-off. From a climate change perspective, food systems and land use are crucial. They account for over a quarter of global greenhouse gas emissions, deforestation, and unprecedented biodiversity loss. However, better land- and water-use planning, strengthened governance, policy reform, technological innovation and investment could deliver around a third of the mitigation the world needs by 2030 and help achieve the Paris Agreement’s long-term goal of keeping the rise of average global temperatures to “well below 2°C”.
Most countries lack tools for integrated land use planning that take account of the complex synergies and trade-offs between agriculture, water, land use, biodiversity, healthy diets, and greenhouse gas emissions. Integrated assessment models which couple together multiple sectoral models to simulate some of these interdependencies have been developed at the global and European levels. However, such models are generally applied at very coarse spatial resolutions whereas land management decisions are taken at finer spatial scales. This PhD will develop methods for downscaling a global/European integrated assessment model to the UK. This could include integrating it with other land use modelling approaches more appropriate to capturing fine resolution processes and interactions. It could also include testing new machine learning techniques that automatically refine or improve fine resolution simulations based on new land use data. The PhD will also contribute to the development of methods to interface the UK model with multiple country versions from different parts of the world as well as the global model. Crucially, this will enable international trade flows in agricultural and forest commodities to be integrated into national decision-making, ensuring the UK and other countries do not meet their national goals by exporting their environmental footprint. Scenario and pathway analysis will be undertaken with the interfaced multi-scale models to inform sustainable food and land use systems in both national and international contexts accounting for the full propagation on uncertainty across model components and scales.
The PhD will collaborate with the FABLE Consortium, which mobilizes top knowledge institutions from G20 and other countries to support the development of the data and modelling infrastructure for long-term pathways towards sustainable food and land use systems. Currently 22 countries are involved in FABLE modelling activities covering 62% of the world’s population (including China, India, Brazil, Ethiopia, USA, Australia, Argentina and Indonesia). Hence, the PhD student will have the opportunity to interact with modellers and data scientists from these countries, as well as IIASA and the UN Sustainable Development Solutions Network (SDSN) who coordinate FABLE. This is also a unique opportunity to undertake a PhD with significant impact as outputs will help inform national policy debates in real time as well as the intergovernmental processes on climate.