Processing and analysing Enose data

Processing and analysing time series and cross-sectional data from electronic soil sensors (Enoses)

Degree type


Closing date

29 October 2021


Hobart, Launceston

Citizenship requirement

Domestic/International Onshore

About the research project

This PhD project is about processing and analysing data from TIAs current soil sensor (Enose) investigations.  The wider Enose project is based on a low-cost electronic nose, which is designed to detect soil volatile compounds, for use as a proxy of soil condition. We have high-level evidence that the signals produced by this device change under differing soil conditions and various mixes of known organisms. In addition, there is evidence the eNose can detect volatiles that are related to soil biological activity and function.

The processing objectives of the PhD project are to develop the data analysis and visualisation tools required to transform the raw eNose data into usable output (for both farmers and researchers).

The analysis objectives of the PhD project are to determine the relationships between soil condition and eNose signals.

The data generated by the eNose is unlike any existing soil data.  The eNose signals are arrays of high-frequency time-series measurements, which correspond to both lower-frequency time-series data sets (e.g. weather and farmer interventions), and cross-sectional background and scientific knowledge (e.g. soil classifications and traditional microbiological analysis).  Analysing and modelling this unique form of ‘big data’, in both cross-sectional and time-series dimensions, will require innovative methodologies – drawing from the fields of statistics, econometrics, AI and machine-learning.  In other words, this PhD project is about using data science within applied agricultural research.

Ultimately, the eNose signals need to be incorporated into predictive and explanatory models for soil condition metrics.  The PhD project will focus on models of “soil resilience” currently under development in a project funded by the Soil CRC.   Models will initially need to be developed with relatively limited data sets. This means that a key technical objective of the PhD project is to enhance the robustness and validity of in-sample model analysis (using everything that we have for estimation and assessment of models), without having to fall back on out-of-sample tests or cross-validation analysis (on which machine-learning methods rely by default).

Primary Supervisor

Meet Dr Ian Hunt


Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

  • a living allowance stipend of $28,597 per annum (2021 rate, indexed annually) for 3.5 years
  • a relocation allowance of up to $2,000
  • a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.


The project is open to domestic (Australian and New Zealand) and international applicants who are already in Australia (onshore) at the time of submitting their application.

Due to current Australian COVID-19 travel restrictions the University cannot accept applications from international applicants who are currently overseas.

Applicants should review the Higher Degree by Research minimum entry requirements .

Applicants from the following disciplines are encouraged to apply:

  • Data Science
  • Statistics
  • Econometrics

Selection Criteria

The project is competitively assessed and awarded.  Selection is based on academic merit and suitability to the project as determined by the College.

Additional essential selection criteria specific to this project:

  • First class honours (or equivalent to a distinction/merit in the UK) in the relevant disciplines

Additional desirable selection criteria specific to this project:

  • Undergraduate degree, or significant course work, in a core physical science, mathematics or computer science
  • Coding skills in R, PYTHON or MATLAB

Application process

There is a three-step application process:

  1. Select the project, and check you meet the eligibility and selection criteria;
  2. Contact the Primary Supervisor, Dr Ian Hunt, if you have any questions about the project; and
  3. Click here to submit an application by the closing date listed above.
    • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
    • As part of your application you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.

Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email.

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