Dynamic modelling of agricultural data using functional and statistical methods

You will develop advanced functional and statistical methods for modelling crop phenotypic traits. With the advances in technology in agriculture, a large number of crop traits are easily available with remote sensing techniques at a high spatial and temporal resolution.

You will explore the potential of a variety of statistical functional methods, including linear mixed models and Ordinary Differential Equations (ODEs), to analyse and interpret crop data. This research will investigate the potential of these strategies to leverage crop growth data and biological understanding to extract additional parameters of interest.

You will utilise both Frequentist and Bayesian statistical methodologies. In particular, we will investigate the performance of the Cross Entropy method for optimisation. For the Bayesian inference, we will explore both Markov Chain Monte Carlo, and Sequential Monte Carlo methods such as Approximate Bayesian Computation.

This position will be based at the St Lucia campus of UQ. This project is part of the multi-institutional multi-disciplinary Analytics for the Australian Grains Industry (AAGI) initiative, developed by UQ, Curtin University, and the University of Adelaide to harness analytics to drive the sector's profitability and global impact.

You will enjoy the opportunity to work in a multidisciplinary team.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

Your application will be assessed on a competitive basis.

We take into account your:

• previous academic record
• publication record
• honours and awards
• employment history.

A working knowledge of statistics and programming would be of benefit to someone working on this project.

You will demonstrate academic achievement in the fields of agricultural sciences and mathematics and the potential for scholastic success.

A background or knowledge of statistical time series analysis is highly desirable.

You must submit an expression of interest (EOI) by 31 March, 2026 31 March, 2026.

Before you apply

1. Check your eligibility for the Doctor of Philosophy (PhD).
2. Prepare your documentation.
3. If you have any questions about whether the project is suitable for your research interests, contact Dr Slava Vaisman (r.vaisman@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

1. Are you applying for an advertised project: 'Yes'
2. Project: 'Research project scholarship'
3. Scholarship Code Listed in the Advertisement: AGRICULTURAL-VAISMAN
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/dynamic-modelling-agricultural-data-using-functional-and-statistical-methods-0

Submit an EOI