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2018 NPIF Studentships - Full Details

Below are the studentships available for October 2018 entry – Application deadline Monday 18th June 2018



General information for applicants
Using data science to develop the next generation of soil models

We are looking for an enthusiastic and numerate student who is keen to explore the cutting-edge of soil and data science.

Rapidly increasing volumes of environmental data and advances in data processing, such as machine learning, offer exciting prospects for developing a new generation of soil models that can push the boundaries of our understanding of soils and ability to predict and sustainably manage their future.

In this studentship, you will:

  • Experiment with data science methods, creating and testing new data-driven soil models, and combining these techniques with more traditional process-based approaches.

  • Gain a highly valuable skillset in data science and environmental modelling.

  • Be supported by a supervisory team with expertise in data science, environmental statistics, and soil modelling, and receive professional training in these areas.

  • Join a diverse community of soil and data scientists at Lancaster University and the Centre for Ecology & Hydrology Lancaster.

Supervisor: Prof Gordon Blair
Registered: Lancaster University

Eligibility: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in relevant subjects such as (but not limited to) Natural Sciences, Geography, Environmental Sciences, Statistics, or Computer Sciences. The project will best suit a student with a strong quantitative background and experience or enthusiasm for gaining skills in programming.

Only open to UK/EU residents. 

For further details please contact Prof Gordon Blair (g.blair@lancaster.ac.uk) and Dr Jess Davies (jess.davies@lancaster.ac.uk)




A machine learning approach to predicting soil structural dynamics and the implications for function using X-ray imaging

The application of X-ray imaging from the medical sciences to soil sciences has revolutionised research in the area. The ability to observe 3D soil structure, undisturbed, as it exists in the field, represents a major advance in our need to understand how the soil pore space influences important functions such as infiltration, gas diffusion and the ability of roots to grow within it. A current limitation is that the imaging approaches often provide more data than can handled by conventional computing. In this project we will apply the principles of Artificial Intelligence (in particular, machine learning) for the first time, to assess large image data sets and use them to identify the key features that describe a soils structure, and in particular, those properties that influence how roots grow in suboptimal conditions when the soil is too hard and those that influence how agrochemicals like pesticide leach through soil.

 This project will be based at University of Nottingham

Supervisor: Prof Sacha Mooney
Registered: University of Nottingham

Eligibility: First-class or 2.1 (Hons) degree or Masters degree (or equivalent) in subjectsuch as Environmental science, Geography or Natural Sciences. 

Only open to UK/EU residents. 

For further details please contact Professor Sacha Mooney; sacha.mooney@nottingham.ac.uk





Tracing the origin of sediments and C across the terrestrial–aquatic continuum: a holistic approach to assess climate change and water quality threats

Unravelling the spatial and temporal dynamics in organic carbon (OC)transport at the land-water interface should be considered as a key domain of research in order to tackle future soil fertility and water quality degradation threats. This project aims to address this challenge by developing a novel framework to assess the impact of erosion induced fluxes of OC on ecosystem services across 2 contrasting catchments in Scotland. In particular, modern large databases, including land use, soil type, vegetation productivity (MODIS satellite imagery) and topography, as well as a novel plant-derived tracing technique based on compound specific stable isotope analyses will be considered. The PhD candidate will be offered the opportunity to work in an interdisciplinary team, crossing the boundaries of biogeochemistry, hydrology and soil science, and is expected to fulfil a diverse portfolio of tasks enabling the development of excellent research skills, such as data analysis, GIS, programming and modelling.

Supervisor: Jeroen Meersmans
Registered: Cranfield University

Eligibility: Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in subjects such as GIS, soil science, remote sensing techniques and statistical analysis. Applicants with aptitude for field work and experience with a programming language (e.g. Matlab, R) and GIS-software package (e.g. ARC-GIS)are particularly welcome. The successful candidate will be expected to cooperate closely with other members of the research team.

Only open to UK/EU residents. 

For further details please contact Jeroen Meersmans, Jeroen.meersmans@cranfield.ac.uk






Unravelling heterogeneous soil greenhouse gas emissions using artificial intelligence

Soil greenhouse gas (GHG) emissions are heterogeneous in space and time meaning that accurate quantification of GHG’s are confounded by our limited capability to measure reliability at relevant spatio-temporal scales. A recent advance in robotic GHG measurement technology (http://www.skyline2d.uk/) is allowing us for the first time to reduce this uncertainty around the highly potent GHG nitrous oxide (N2O)1.

The overall objective for this PhD is to develop an artificially intelligent system to optimise sampling strategies for Skyline2d. You will devise new sampling algorithms to focus measurements on areas of greatest parameter uncertainty within spatial-temporal N2O emissions models – so called adaptive sampling (AS). Specific training will be provided in statistical and data intensive methods. Whilst the main focus of the work is on developing the sampling algorithms there will be opportunities to work closely with UK soil scientists (http://assist.ceh.ac.uk/) for deploying the Skyline2d system.

1. https://onlinelibrary.wiley.com/doi/abs/10.1111/gcbb.12491

 This project will be based at CEH Lancaster.

Supervisor: Dr. Peter Henrys
Registered: Lancaster University

Eligibility: The position is ideal for a student interested in a mix of desk and field based activities and in cross disciplinary working. The student should be motivated by applying novel methodological developments to solve complex scientific challenges. 

Applicants should hold a minimum of a UK Honours Degree at 2:1 level or equivalent in a relevant subject such as Environmental Science, a quantitative subject or Computer Sciences

Only open to UK/EU residents. 

For further details please contact Dr. Peter Henrys:pehn@ceh.ac.uk