ARC Research Hub for Medicinal Agriculture

Applications for these scholarships are now open.

ARC Research Hub for Medicinal Agriculture PhD Scholarships

The ARC Research Hub for Medicinal Agriculture (ARC MedAg Hub) works to apply knowledge and leading-edge innovation by transforming high quality, plant-derived therapeutics production into an integrated industry spanning primary producers and manufacturers. The ARC MedAg Hub is a multidisciplinary collaboration conducting pre-clinical research in medicinal agriculture amongst researchers at La Trobe University, The University of Melbourne, Olivia Newton-John Cancer Research Institute, in collaboration with funding agency the Australian Research Council and leading industry partners Cann Group Limited, Hexima, Photon Systems Instruments, SensaData, UTT BioPharma, Bio Platforms Australia and Palo Alto Research Center Inc.

The ARC MedAg Hub is offering several PhD Research Scholarships for outstanding candidates to undertake research aimed at improving the profitability and sustainability of medicinal agriculture for primary producers and adding value for pharmaceutical manufacturers and end-users.

Eligibility

In addition to the standard eligibility requirements for La Trobe Graduate Research Scholarships, applicants must also meet the following criteria:

  • have an interest in subjects related to medicinal agriculture or plant biology, such as; breeding, genetics, high-throughput imaging and phenomics, genome regulation, metabolomics, integrative ‘omic analysis, biochemistry, secondary/specialized metabolism.
  • be an Australian and New Zealand citizen or Australian permanent resident for all projects with the exception of Project - Prediction and evaluation of plant traits through the application of machine learning algorithms on hyperspectral phenomics imaging data.

How to apply

Information on how to apply is available on our Graduate Research Scholarships page. To obtain in-principle agreement to apply, or for any other questions about the projects, please email medaghub@latrobe.edu.au.

Please also be aware that:

  • Due to the nature of the research involved, confidentiality and security criteria will need to be agreed to before offers can be secured.
  • Potential candidates may be required to be interviewed as part of the application process.
  • You must complete a Research Statement Form for each project you wish to be considered for. While you may apply for multiple projects, only one scholarship will be awarded per candidate.

More information

For further information about these scholarships or to discuss your suitability, visit the ARC MedAG Hub website, or email medaghub@latrobe.edu.au

Current available research projects

Project title: Healthy plants for human health: tackling plant disease in medicinal cannabis production

Description

Diseases can reduce yield and contaminate medicinal cannabis products with pathogens and agrichemicals. This project aims to control fungal diseases without the use of agrichemicals as well as developing surveillance techniques for early warning of pathogen presence, enabling timely interventions. It will determine whether metabolite engineering of cannabis affects natural disease resistance. The position will be based mainly in Narrabri, NSW and will work closely with cannabis industry representatives.

Special conditions

Preference will be given to graduates with a strong background in/experience with fungal plant pathology.

Project title: Selective liquid-liquid solvent extraction of therapeutically active cannabinoids and terpenes from cannabis

Description

The therapeutic application of cannabinoids and terpenes derived from Cannabis sativa is playing an increasingly significant role in public health and medicine. Cannabis has been described as a veritable ‘treasure trove’, producing more than 100 different cannabinoids and terpenes. This project investigates a liquid-liquid solvent extraction process for the recovery of cannabinoids and terpenes from cannabis plant residues and unwanted impurities that offers a much more cost-effective separation solution over the standard supercritical CO2 extraction, which is characterised by high capital inputs and operating costs. The project involves the evaluation of a range of green solvents and extractants as well as the physical conditions of the separation process in the recovery efficiency of cannabinoids and terpenes. In collaboration with the Australian Research Council and industry partners, the project aims to deliver novel cannabis extraction methods into the pharmaceutical market and drive better outcomes in public health.

Special conditions

This scholarship is only available to domestic applicants with a Bachelor Degree in chemistry or chemical engineering.

Project title: Identification of novel antimicrobials from plants using a silkworm infection model

Description

Essential oils (EOs) are secondary metabolites of plants, obtained by distillation or mechanical processes. Their composition varies not only on the basis of the different botanical species, but also on the part of plants used, the season of harvest, the environmental conditions, and the extraction techniques. Many compounds show marked antimicrobial activity, which makes them promising candidates for novel drugs to treat bacterial and fungal infections. However, in veterinary applications, data concerning both the in vitro susceptibility testing and the in vivo application of EOs for treatments is limited. To increase our discovery of novel antimicrobials from EO will take advantage of silkworm infection model. The silkworm infection model is a suitable model to examine the therapeutic effectiveness of antimicrobial agents as it has conserved immune response. similar pharmacokinetics compared to mammals and no ethical concerns. Will investigate the use of several different EOs extracts against a range of veterinary clinically significant bacteria and fungal pathogens using silkworm model.

Project title: A multi-omics atlas of cannabis tissues

Description

Models allow complex systems to be tested and understood. By finding both regulatory genes and their targets within a biological system we can predict their function in plants (Nature Plants paper). This approach can be used to discover new genes that control traits like growth and yield, and the knowledge applied in biotechnology to improve plant performance. In this project we will generate a multi-omic atlas of cannabis tissues that maps transcript, proteomic, phosphoproteomic and orfeomic data. We will then use the atlas to describe the differing activity of specialized metabolism between cannabis tissues by generating predictive regulatory models and testing them in the lab. Students will get hands-on experience of a range of lab ‘omics techniques. They do not need to know bioinformatics - they will be trained in a range of integrative data analysis approaches that allow big data to be explored and understood. These skills would equip students very well for future employment in agricultural biotech or genomics.

Project title: Is transcriptional regulation of specialised metabolism conserved across diverse species?

Description

Plants produce thousands of specialized metabolites as an adaptation to interacting with their environment. These compounds vary widely between plant species, highly dependent on their biological function and the specific challenges faced by the plant species. The pathways synthesising specialized metabolites are under transcriptional regulation that appears to be dominated by MYB and bHLH transcription factors. However, transcriptional regulation of specialized metabolism has been characterized in very few species. This project will take both lab and bioinformatic approaches to understand how specialized metabolism is regulated in cannabis, opium poppies and many other species (New Phytologist review). It will make use of the recent wealth of genomic and transcriptomic data across a wide range of species (such as the 10k Plants project) to predict then validate the role of master regulatory transcription factors, assessing the degree of functional conservation across distantly related species. Students do not need to know bioinformatics already, but they will get the opportunity to learn this and a range of lab genomic techniques.

Project title: Prediction and evaluation of plant traits through the application of machine learning algorithms on hyperspectral phenomics imaging data

Description

With the establishment of the large-scale phenotyping capacity at La Trobe University, an abundance of imaging data is expected to be generated using RGB, thermal, fluorescent and hyperspectral imaging systems. Compared to other imaging techniques, a hyperspectral approach substantially increases data size as well as the complexity of evaluation. It becomes increasingly difficult for humans, even with sophisticated data processing algorithms, to synthesise information in order to successfully predict plant trait trends. This project aims to investigate and augment the latest research in neural networks/machine learning algorithms to better predict plant development and evaluate plant traits based on the visible and near-infrared hyperspectral data.

Project title: Micro-climate modelling of in-door growing conditions of medicinal crops using sensor fusion

Description

The yield and potency of medicinal agriculture crops can be affected by the microclimate of individual plants within indoor cultivation practices. The research will involve undertaking targeted research to develop microclimate modelling using sensor fusion and developing a complimentary expert systems model for use in real-time monitoring and control systems of desired growing conditions. The work will include integration of sensor systems developed by our Hub partners SensaData and PSI, and subsequent development of computation models for microclimate that are co-developed in collaboration with agricultural scientists.

Special conditions

PhD candidates will need to have a background in either Electronic Engineering or Computer Science.

Project title: Developing high-throughput 3D phenomics methods

Description

Plant 3D architecture has an important role in determining plant performance and yield. However, due to the complexity of plant structures and limitations of both computing and sensor technologies, high-throughput analysis of plant 3D architecture has been very challenging. Recent technological advances have made it possible to map the positions of plant structures at very high resolution. This project will focus on developing methods for high-throughput phenomic analysis of medicinal plant architecture and how this is influenced by environmental conditions.