The Analytics for the Australian Grains Industry (AAGI) initiative is a five-year strategic partnership to enhance the profitability and global competitiveness of the Australian grains sector through advanced analytics. This five-year initiative (2023-2027) is mainly funded by the Grains Research and Development Corporation (GRDC) with a $36 million investment and builds on the previous Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of Adelaide, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data fusion, and statistics to support grain growers in making data-driven decisions. With a total co-investment of $56 million from the three strategic partners, this project offers HDR students a unique opportunity to contribute to cutting-edge research that has a real-world impact on the agricultural sector.

The Analytics for the Australian Grain Industry (AAGI) Scholarship Program (AU node) is funded by the Division of Research and Innovation as part of the co-investment from the University of Adelaide. This program will support ten full-time PhD students commencing studies from 2025 to 2027.

In 2025, we are recruiting four PhD students for the following Projects:

Project 1: Unifying on-farm data and crop models to enhance tactical crop decisions

Summary: Despite the increasing availability of on-farm data and advances in process-based crop models such as APSIM, their integration often remains limited. This project proposes to get more out of on-farm data streams and process models through their more formal mathematical integration, with the desired outcome being to increase the water and/or nitrogen use efficiency of Australian cropping systems. We propose to use a range of emerging data science approaches within the fields of uncertainty quantification, data assimilation and optimisation under uncertainty, complementing data-driven approaches such as physics-informed machine learning. We will start by focusing on informing nitrogen management decisions (i.e. how much and when to apply), since they remain the largest contributor to the gap in water-limited production potential. This PhD project will contribute to the broader AAGI project “Harnessing emerging data science to unlock crop model potential and achieve production frontiers”.

Project-specific prerequisites: Strong quantitative skills are essential. Candidates with Masters or Honours degrees in the following disciplines, or with equivalent research or work experience will be favourably considered: Data Science; Applied Mathematics; Agricultural or Environmental Engineering, Agricultural Economics, Management and Information Technology.

Number of scholarships: Two

Contact person: Dr Matthew Knowling (matthew.knowling@adelaide.edu.au)

Project 2: Efficient construction and visualization of pangenomes for crops with large genomes 

Summary: Pangenomes are highly relevant for grains RD&E pre-breeding research because they capture the full spectrum of genetic diversity within a species, going beyond the limitations of single-reference genomes. By integrating multiple genomes from different individuals or populations, pangenomes can provide a more comprehensive understanding of gene presence/absence, structural variations, and evolutionary dynamics. 

In this project we will aim to develop novel dynamic programming computational methods for pangenome assembly of diploid and polyploid crop species and benchmark them against other methods such as graph-based methods. This project will combine algorithm development and computational programming with large population genomes. Candidates will work within a vibrant cutting-edge analytics team that delivers technologies back to Australian grains industry research to maximise benefit for farmers and growers. This research project will also be highly collaborative with major partners, Curtin University and University of Western Australia. Through these linkages there will also be an opportunity to develop the research further into a practical web-based visualization tool to represent pangenome and structural variations. 

Project-specific prerequisites: Strong Java programming skills are essential. Candidates with a Masters or Honours degrees in the following disciplines, or with equivalent research or work experience will be favourably considered: Computer and Data Science; Applied Mathematics and Statistics.

Number of scholarships: One

Contact person: Dr Mario Fruzangohar (mario.fruzangohar@adelaide.edu.au)

Project 3: Improving genomic prediction accuracy using causal machine learning approaches

Summary: Traditional genomic prediction methods often rely on correlations between genetic markers and phenotypic traits, which can limit their effectiveness in complex plant breeding scenarios. This project will explore the use of causal machine learning (CML) to enhance the predictive accuracy of genomic prediction models by uncovering the underlying cause-and-effect relationships that drive trait variation. CML integrates machine learning with causal inference techniques such as causal graphical models, instrumental variable analysis, and counterfactual reasoning to better handle high-dimensional, multi-environment datasets typical in plant breeding. The project will focus on applying and evaluating CML methods across a range of genomic prediction challenges, including single-environment, multi-environment, and multi-variate scenarios. The successful HDR candidate will work with large, real-world datasets from Australian grains industry plant breeders and collaborate with experts from the Biometry Hub and the Australian Institute for Machine Learning (AIML). 

This PhD project will contribute to the AAGI HDR program where there will be an opportunity to grow your collaborative research within a vibrant network of national and international AAGI research partners. 

Project-specific prerequisites: Strong quantitative and programming skills are essential. Candidates with a Master's or Honours degree in the following disciplines, or with equivalent research or work experience, will be favourably considered: Data Science, Machine Learning, Applied Mathematics or Statistics, or Computational Biology.

Number of scholarships: One

Contact person: Professor Javen Shi (javen.shi@adelaide.edu.au)

Eligibility: 

Applicants must be Australian citizens, permanent residents of Australia, or international students who are acceptable candidates for a PhD degree at the University of Adelaide. Candidates must have a qualification equivalent to an Australian H1 Honours degree (e.g., a prior research thesis that was at least six months of full-time credit and received an excellent mark, or a first-author publication in a peer-reviewed international journal).

Stipend: 

The scholarship will be for 3.5 years and has a tax-free stipend of $35,300 (indexed annually) per annum. Details of any terms and/or benefits can be found in the attached Conditions of Awards for University of Adelaide Research Scholarships.

Enquiries:

Contact Person: Sandy Khor

School/Discipline of:  School of Agriculture, Food & Wine

Email: sandy.khor@adelaide.edu.au 

Expressions of Interest

Expressions of interest should be emailed to Ms Sandy Khor (sandy.khor@adelaide.edu.au) with the name of scholarship in the subject heading. Please ensure you include all of the following documents: 

• Evidence of Australian or New Zealand citizenship, or Australian permanent resident status (if applicable)

• Degree certificates (testamurs)

• Academic transcripts

• Translations of non-English documentation

• Evidence of English language proficiency

• Curriculum vitae