Development of a soil contamination spatial inference system

Summary

The successful scholarship candidate will work on a project to design a spatial inference system for optimized detection and delineation of soil contaminants.

Supervisor(s)

Dr Brendan Malone

Research Location

Sydney Institute of Agriculture

Program Type

PHD

Synopsis

Leveraging outputs from a soil spectral inference model that predicts soil contaminant concentrations through the co-joint use of infrared and XRF spectroscopy, the candidate will develop a digital mapping and predictive system that will be operative for in-field situations. The candidate will work on interesting applied problems related to soil contaminant delineation such as geostatistical treatment of non-Gaussian variables with associated uncertainty quantification. The will investigate these problems using such tools as robust geostatistical models, non-linear modeling, machine learning and more. They will be expected to develop inference models that are applicable in three dimensions. Critical to the project, the candidate will develop an adaptive field sampling protocol that couples spectral inference and spatial inference to devise an optimal sampling of contaminated sites for subsequent contaminant delineation. The successful candidate will be mentored by a skilled team of geospatial soil scientists from the University of Sydney and Charles Sturt University; namely, Prof. Budiman Minasny (USYD), Assoc. Prof. Tom Bishop (USYD), Prof. Alex McBratney (USYD), Dr Brendan Malone (USYD) and Dr. Anna Horta (CSU). The candidate will also be resourced by a diverse faculty and student cohort with strong skills in soil science and data science in general.  Successful completion of the PhD will provide the candidate with strong generic skills in data science, with specialist skills in the analysis of spatial data.

Additional Information

Additional supervisors:
Prof Alex McBratney
Prof Budiman Minasny
Assoc Prof Thomas Bishop 




HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

soil science, soil contamination, spatial statistics, data science, inference systems

Opportunity ID

The opportunity ID for this research opportunity is: 2143