CSIRO Postdoctoral Fellowship in Quantitative Genetics for Plant Breeding using ML and AI - [Archived Advertisement]

Acknowledgement of Country

CSIRO acknowledges the Traditional Owners of the land, sea and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection to their culture and pay our respects to their Elders past and present. View our vision towards reconciliation

Child safety

CSIRO is committed to the safety and wellbeing of all children and young people involved in our activities and programs. View our Child Safe Policy.

The opportunity

• Launch your career at Australia’s National Research Organisation
• Great opportunity for an early career researcher with a PhD in quantitative or statistical genetics, biostatistics, data science, computational biology, genomics or plant breeding.
• Join the CSIRO Agriculture & Food Breeding Innovations Group

CSIRO Early Research Career (CERC) Postdoctoral Fellowships provide opportunities to scientists and engineers who have completed their doctorate and have less than three years of relevant postdoctoral work experience. These fellowships aim to develop the next generation of future leaders of the innovation system.

CSIRO Agriculture & Food's Breeding Innovations Group requires a forward-thinking Quantitative/Statistical geneticist to join the CSIRO Cotton Breeding Program.

This successful candidate will develop and validate modern statistics, machine learning and artificial intelligence approaches for predicting field performance of cotton plants and breeding lines using large scale genomic and phenomic data, as well as assessing the value of incorporating environment, ancestry, and other omic data streams into a genomic selection model.

Your duties will include:

• Carry out innovative, impactful research of strategic importance to CSIRO that will, where possible, lead to novel and important scientific outcomes.
• Designing and implementing robust statistical approaches and computational pipelines to model the relationships between cotton genotypes and their field-based phenotypes (yield and fibre quality) from rainfed production systems across various growing seasons and environments.
• Recognise and utilise opportunities for innovation and the generation of new theoretical perspectives, and progress opportunities for the further development or creation of new lines of research.
• Analysis of prediction accuracies of genomic selection models for different cotton agronomic traits and the refinement of the models or approaches to incorporate environment, pedigree, phenomics and additional -omic data.
• Record, manage, and analyse cotton genotype and phenotype data as well as pedigree, environmental, additional phenomic and other omic data sets. This will require close collaboration with the teams generating the data to ensure that it remains relevant for both conventional and advanced genetic approaches to cotton improvement.
• Explore the possibility to apply emerging ML and AI technologies such as deep learning and large language models in quantitative genomic research.

Location: Canberra, ACT
Salary: AU$96k - AU$105k plus up to 15.4% superannuation
Tenure: Specified term of 3 years
Reference: 98435

To be considered you will need:

• A doctorate (or will shortly satisfy the requirements of a PhD) in a relevant discipline area, such as quantitative or statistical genetics, biostatistics, data science, computational biology, genomics or plant breeding. Please note: To be eligible for this role you must have no more than 3 years (full-time equivalent) of postdoctoral research experience.
• Demonstrated skills in the handling and analysis of large biological datasets, including estimating genomic relatedness, conducting phenotype genotype association analysis, and predicting phenotype outcomes using high-dimensional statistical predictive models.
• An understanding of and experience with statistical and machine learning methods for phenotype prediction based on high dimensional genomic data such as high dimensional linear and non-linear regression, random forest, Gaussian process model, neural network and deep learning.
• Evidence of advanced programming skills in languages and statistical software packages relevant to biostatistics and bioinformatics (e.g. R, Python, SAS or equivalent).

For full details about this role please view the Position Description

Eligibility

Applications for this position are open to Australian/New Zealand Citizens, Australian Permanent Residents or you must either hold, or be able to obtain, a valid working visa for the duration of the specified term (visa sponsorship and relocation assistance may be provided to the successful candidate if required).

Appointment to this role is subject to provision of a national police check and may be subject to other security/medical/character requirements.

Flexible working arrangements

We work flexibly at CSIRO, offering a range of options for how, when and where you work. 

Diversity and inclusion

We are working hard to recruit people representing the diversity across our society, and ensure that all our people feel supported to do their best work and feel empowered to let their ideas flourish. 

About CSIRO

At CSIRO Australia's national science agency, we solve the greatest challenges through innovative science and technology. We put the safety and wellbeing of our people above all else and earn trust everywhere because we only deal in facts. We collaborate widely and generously and deliver solutions with real impact. 

CSIRO is committed to values-based leadership to inspire performance and unlock the potential of our people.

Join us and start creating tomorrow today!

How to apply

Please apply on-line and provide a cover letter and CV that best demonstrate your motivation and ability to meet the requirements of this role.

Applications close

1 December 2024, 11:00pm AEDT