Are you a passionate researcher eager to propel the future of agriculture through the power of machine learning (ML)? Do you possess a keen understanding of plant science and a yearning to contribute to global food security? Look no further! The University of Queensland (UQ) in Australia is seeking a driven Postdoctoral Research Fellow to join a groundbreaking project focused on Crop Trait Prediction with Machine Learning.
About This Opportunity
AAGI is a five-year strategic partnership across UQ, Curtin University, and the University of Adelaide that is aimed at harnessing analytics to drive the sector’s profitability and global impact. AAGI gives Australian growers an opportunity to be world leaders in analytics-driven decision making to drive efficiency and precision and support farm enterprise risk management.
This is a fixed-term position for up to 3 years. This postdoctoral fellowship position based in the School of Mathematics and Physics will play a lead role in developing crop trait prediction solutions based on physics informed machine learning to allow accurate predictions by combining physical knowledge and data. Methods will include use of deterministic crop models to generate training data for PIML, and testing against observed datasets. In addition to AAGI, this position will service research needs of other UQ and GRDC supported projects.
Responsibilities:
Produce quality research outputs consistent with discipline norms by publishing or exhibiting in high quality fora.
Work collaboratively with AAGI, UQ and grains industry colleagues to deliver research, analytical support and teaching activities
Provide analytical support for research projects supported by AAGI and which are aligned with the expertise of the applicant
Contribute to transfer of knowledge, technology and practices to colleagues and to researchers within the Australian grains industry
Review and draw upon best practice research methodologies
Contribute to the effective supervision of Honours and Higher Degree by Research students
On an as-needs basis:
Provide feedback, coaching, and professional development to others in the research teams, including to Service and Support of AAGI investments
Manage research support staff effectively throughout the employee lifecycle in accordance with University policy and procedures
Work to promptly resolve conflict and grievances when they arise, in accordance with University policy and procedures.
Demonstrate citizenship and leadership behaviours that align to the UQ values.
Provide support to other academic positions and unit operations as needed during other team members absences.
Contribute to internal service roles and administrative processes as required, including participation in decision-making and service on relevant committees.
Collaborate in service activities external to the immediate organisation unit.
Develop external links and partnerships by cultivating relationships with the grains industry, government departments, professional bodies and the wider community.
Benefits
This is a full-time (100%), fixed-term position for up to 3 years at Academic level A. The full-time equivalent base salary will be in the range $77,324.85 - $102,945.12 plus a generous super allowance of up to 17%. The total FTE package will be up to $90,470.08 - $120,445.79 annually.
The greater benefits of joining the UQ community are broad: from being part of a Group of Eight university, to recognition of prior service with other Australian universities, up to 26 weeks of paid parental leave, 17.5% annual leave loading, flexible working arrangements including hybrid on site/WFH options and flexible start/finish times, and genuine career progression opportunities via the academic promotions process.
You must have unrestricted work rights in Australia for the duration of this appointment. Visa sponsorship may be available for this appointment.
REQUIREMENTS
Completion or near completion of a PhD in the discipline area
In-depth understanding of fundamentals of machine learning and deep learning, and proficiency in using machine learning or deep learning libraries such as scikit-learn, PyTorch, Tensorflow
Advanced skills in computer programming for data manipulation and analysis methods including multi-dimensional analytics of large temporal and/or spatial datasets
Experience in use of multi-modal techniques in forecasting and prediction in time series
Experience in the principles of crop growth models and use of crop simulation software is desirable.
Demonstrated success in collaboration in interdisciplinary team-work environments
Evidence of publications in reputed refereed journals and presenting at conferences.
Evidence of capacity to develop and deliver educational or training materials
Demonstrated experience in successful supervision of undergraduate and/or higher degree research projects.
Want to Apply?
All applicants must upload the following documents in order for your application to be considered:
Resume
Cover letter
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