Postdoctoral Fellowship in Aquaculture Genetics

Job Details

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

Working with a collaborative, multidisciplinary team, you’ll translate your research into practical decision-support tools that help breeders balance genetic gain, resilience, adaptability, sustainability and genetic diversity. With scientific, technical and industry support, you’ll develop new ideas, build your research capabilities and deliver outcomes with practical value for the aquaculture industry.

Your high-level duties will include: 

• Develop and evaluate next-generation quantitative genetic and genomic methods for aquaculture breeding, including multi-trait, multi-environment and G×E×M prediction.
• Develop multi-objective breeding optimisation approaches that balance genetic gain, resilience, sustainability, profitability and genetic diversity.
• Develop and test Look-Ahead Genomic Selection and haplotype-stacking approaches to improve long-term breeding outcomes across generations.
• Build simulation and optimisation frameworks to evaluate breeding strategies under changing environmental, disease, management and production scenarios.
• Integrate genomic prediction, optimisation, mate allocation and look-ahead decision methods into practical breeding decision-support tools.
• Validate the methods using simulated and real aquaculture datasets and assess their transferability across species and production systems.
• Develop reproducible research software, computational pipelines and technical documentation.
• Publish and communicate research findings, contribute to collaborative research and industry engagement, and support future research and funding opportunities.

Requirements

• A doctorate (or will shortly satisfy the requirements of a PhD). The doctorate must be in a relevant discipline area, such as quantitative genetics, animal breeding, plant breeding, statistical genetics, genomics, or a closely related discipline.

Please note: To be eligible for this role you must have no more than 3 years (or full-time equivalent) of relevant research experience.

• Strong knowledge of quantitative genetics and/or genomic prediction.
• Demonstrated expertise in statistical modelling and computational analysis.
• Strong programming skills in at least one scientific programming environment such as R, Python or C++.
• Experience analysing large genetic, genomic or biological datasets.
• Develop novel algorithms for multi-stage and long-term breeding decision optimisation.
• Design and implement scalable computational methods capable of analysing large genomic and phenotypic datasets.
• Apply advanced statistical learning, machine learning and artificial intelligence approaches where appropriate to improve prediction and decision-making performance.
• Develop efficient software implementations using modern programming practices, parallel computing and reproducible research workflows.
• Evaluate optimisation and prediction methods under uncertainty using simulation-based experimentation and sensitivity analysis.
• A record of high-quality scientific publication appropriate to career stage.
• Demonstrated ability to work effectively within collaborative and multidisciplinary research teams.
• Strong scientific writing, communication and organisational skills.

Essential criteria

• Experience in one or more of the following: multi-objective optimisation, machine learning, Bayesian statistical modelling, stochastic simulation or climate-resilience modelling.

Contact Information

Applications close 

24 November 2026