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PhD Scholarship - Crop Genomics

Apply now Job no:505070
Area:Qld Alliance for Agriculture and Food Innovation
Salary (FTE):School Schol (APA Rate) NONBAND ($27,082.00 - $27,082.00)
Work type:Full Time - Fixed Term
Location: St Lucia, Brisbane

Queensland Alliance for Agriculture & Food Innovation

The Queensland Alliance for Agriculture and Food Innovation (QAAFI) is a research institute of the University of Queensland (UQ) which was established in 2010 and comprises of four research centres – the Centre for Crop Science, the Centre for Horticultural Science, the Centre for Animal Science and the Centre for Nutrition and Food Sciences.

QAAFI’s team of 450 researchers, postgraduate students and support staff undertake high impact science for agriculture and food industries. The institute’s strong partnership with the Queensland Government provides our researchers with a direct link to the agriculture industry in Queensland, and world class field research facilities throughout Queensland. Agriculture is one of UQ’s highest ranked research fields nationally and internationally and QAAFI is a global leader in agricultural research in subtropical and tropical production systems.

QAAFI scientists are driven to make a difference to the agriculture and food industries and have over 150 collaborators worldwide.

Details of the research interests of the Institute may be accessed on the Institute’s web site at

Research Area

The human population is expected to reach nine billion by the year 2050 and will strain global resources. The efficiency of crop breeding must improve if we are to develop resilient varieties that maintain productivity and meet future demands.

Genomic selection (GS) is a modern animal and plant breeding technology which uses genome-wide DNA marker data to predict the agronomical performance of individuals or variety candidates. GS had significant impacts on animal and plant production worldwide because it enables breeders to select superior individuals as soon as they are generated, rather than after years of expensive field trials. This helps to rapidly speed up the breeding cycle and to realise more genetic gain. With GS, a statistical model is trained with phenotypic and genotypic data from a relevant population. This model is used to predict the breeding values of new selection candidates that have been genotyped but not phenotyped. While GS research in plants has expanded and has led to many new GS modelling approaches, the application of GS in breeding programs of wheat and sugarcane has been limited by a lack of research about implementation strategies, and by relatively low prediction accuracies when predicting new selection candidates. This is somewhat because most agronomically important traits are highly quantitative with very complex genetic architectures, characterized by low heritabilities and high genotype-by-environment (G × E) interactions, particularly in Australian environments. Because plant breeding programs are very complex, cost-intensive and rigid endeavours, it is almost impossible to test novel breeding strategies empirically. Computer simulations are a powerful tool to assess and compare the potential impacts of different breeding strategies on genetic improvement and development of available genetic diversity over time. For crops like wheat, however, suitable simulation tools which are able to capture the full complexity associated with biological processes like meiosis are limited.

Under the supervision of Professor Ben Hayes, the co-inventor of genomic selection, ( and other geneticists and bioinformaticians at UQ, the PhD student will aim to develop and implement new approaches for genomic prediction, gene discovery models and tools to simulate genetic processes and breeding strategies. This will involve using large genomic and phenotype data sets in combination with reproductive strategies to reduce breeding cycles, in order to accelerate the rate of improvement in key agricultural traits across a number of species. This will also include extensive computer simulations to model genetic processes relevant to plant breeding programs. The student is expected to contribute to leading programs of applied research in the area of genomic prediction and genetic simulation, working across major Australian crops like wheat, barley and sugarcane. The student will gain experience working with industry scientists on projects targeting implementation of genomic prediction in their agricultural species.

The role

There is an opportunity for a highly motivated PhD student to join the QAAFI team working on developing and implementing innovative genomic selection and genetic simulation strategies for some of Australia’s most important crops.

The Person

Applicants will have a First Class Honours degree or equivalent and should be eligible for a Category 1 scholarship or equivalent. Basic expertise and experience is required in one or more of the following areas: plant genetics, quantitative genetics, statistics, mathematics or computer programming. Applications from related life science disciplines, like mathematics and/or statistics are strongly encouraged. Expertise in one or more scripting/programming languages like R, Python, C(++) and/or experience with Linux is a plus.

Applicants must fulfil the PhD admission criteria for the University of Queensland, including English language requirements, and demonstrate excellent capacity and potential for research.  Entry requirements can be found at:   

Strong academic performance demonstrated through publication output in peer reviewed international journals is highly desirable.


Appointment to the position is contingent upon receipt of a Category 1 scholarship through one of UQ's scholarship rounds. The prospective student will be provided with assistance to apply for the Category 1 scholarship.  The scholarship rate is AUD$27,082 per annum (2019 RTP rate, indexed annually) tax-free for three years with the possibility of a six month extension in approved circumstances.

For further information on scholarships please refer to


To submit an application for this role, use the Apply button below. All applicants must supply the following documents:

The successful applicant must be able to make their full online application to the UQ graduate school ( by Friday 28th September 2018.


Further information regarding the project can be obtained by contacting Ben Hayes, Professor in Agri-genomics at QAAFI at +61 7 3346 2173 or

For general information on the research higher degree program, see

Applications close: E. Australia Standard Time

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