Dynamic modelling of agricultural data using functional and statistical methods

The major objective of this project is to 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. This PhD project aims to 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.

In this project, both Frequentist and Bayesian statistical methodologies will be utilised. In particular, we will investigate the performance of the Cross Entropy method for optimization. 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. The successful applicant 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 field/s of agricultural sciences and mathematics and the potential for scholastic success.

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

Before you apply

Before submitting an application you should:

1. check your eligibility for a Doctor of Philosophy (PhD)
2. prepare your documentation
3. contact Dr Slava Vaisman (r.vaisman@uq.edu.au) to discuss your interest and suitability
4. submit your application by 15 April, 2025 15 April, 2025.

When you apply

You apply for this scholarship when you submit an application for your program. You don’t need to submit a separate scholarship application.

In your application ensure that under the ‘Scholarships and collaborative study’ section you select:

• ‘My higher degree is not collaborative’
• ‘I am applying for, or have been awarded a scholarship or sponsorship'
• ‘Other’, then ‘Research Project Scholarship’ and in the ‘Name of scholarship’ field enter AGRICULTURAL-VAISMAN.

Apply now