Application of machine learning to plant genomics

Application of machine learning to plant genomics

The project aims to apply the latest machine learning methods to understand plant genome structure and function.

There has been a rapid growth in the quality and quantity of data for plant genomes, but methods to interrogate this data are lacking. At the same time, machine learning and artificial intelligence methods are revolutionising data analysis. This project will apply the latest machine learning methods to gain a greater understanding of plant genomes, their diversity, function and evolution.

In this project, PhD students will learn how to apply machine learning to understand plant genome structure and function using genomic data and high-performance computing. 

Research team leader: Professor David Edwards

Professor David Edwards leads an applied bioinformatics group focussing on understanding plants and their genomes for understanding plant evolution and adaptation and improving agriculture. He applies a range of computational methods using genome sequence and plant image data with high-performance computing.

Suggested readings

Professor Edwards publications can be accessed via Google Scholar

Funding

The project funding is $32,000 per annum (tax-free) paid to the successful candidate

How to Apply

Check criteria

• To be accepted into the Doctor of Philosophy, an applicant must demonstrate they have sufficient background experience in independent supervised research to successfully complete, and provide evidence of English language proficiency
• Requirements specific to this project: Students are expected to have a background and interest in biology as well as a solid understanding of Linux

Submit enquiry to research team leader 

• Contact the research team leader via the Expression of Interest button below
• After you have discussed your project with the research team leader, contact [email protected] to proceed with your application

Submit Expression of Interest