Centre-wide Research Fellow in Prediction

• The School of Biological Sciences

• Academic level B/C

• Full Time/Fixed-term position for up to 4 years

• Closing November 21 2022 at 11.00pm AEST (R-18352)

About This Opportunity 

This senior research position on prediction will work with several Chief Investigators in the CoE to enhance the CoE’s predictive capacity in the areas of phenotype, genome, transcriptome and trait prediction. The position will report to the Centre Director who will work with the Centre Executive and related committees to ensure the research is prioritized to address important knowledge/knowhow gaps in the Centre and to support multidisciplinary collaborations.

The primary purpose of this position is to improve prediction for genomic selection, omic analysis (e.g. transcriptome analysis of mechanisms), trait analysis (e.g., hypothesis testing) and their integration.  The position will carry responsibility to identify areas across the CoE that require innovation and collaboration in this area and then to work collaboratively to achieve this. Plant systems may include Arabidopsis, sorghum, senecio, Marchantia and other systems prioritized by the CoE, its Partners and collaborators.

 Key responsibilities include:  

Research

• Innovate the Centre’s research in areas such as (i) directed networks, (ii) analysis and connectivity of networks across data layers including genomic, mechanistic and trait data and (iii) genomic prediction, (iv) machine learning.

• Undertake design of experiments, synthesis of data, new analyses, undertaking and reporting on literature searches.

• Preparation and leadership of scientific papers from the research.

• Actively undertake or contribute to collaborative, multidisciplinary research, including plant biology and quantitative biology in the predictive space of the CoE.

• Communicate research outcomes, in the form of oral and written presentations to stakeholders including research partners and collaborators, at meetings, in reports, conferences, and in highly ranked peer-reviewed publications, where agreed.

• Participate in industry engagement activities.

• Take a chief investigator role in applications for external research funds; and contribute to funding proposals, including research grant applications and tenders.

• Provide support for the Centre of Excellence in access and the development of prediction capacities within the National High-Performance Computing Infrastructure.

Additional Duties at Level C

• Independently develop collaborations with researchers across the Centre and across different nodes as well as with our Partners (such as CSIRO).

• Lead collaborative activities and applications for external research funds.

• Act as primary supervisor of multiple students.

• Supervise junior postdoctoral researchers.

• Lead development of software and resources for the CoE and wider research community.

• Enhance collaboration between mathematicians and biologists in the CoE.

• Provide strategic, technical and scientific leadership for the Centre of Excellence in access and the development of multiple prediction capacities within the National High-Performance Computing Infrastructure.

Supervision and Researcher Development

• Act as Principal Supervisor to Higher Degree by Research students.

• Demonstrates and leads others in the responsible conduct of research.

• Demonstrates personal effectiveness in supervision and management and development of researcher capability and skill.

• Lead the facilitation of engagement opportunities for supervisees

To view the Position Description in full, please email [email protected].

About ARC Centre of Excellence for Plant Success in Nature and Agriculture 

 

The Australian Research Council (ARC) Centre of Excellence (CoE) for Plant Success in Nature and Agriculture is a research Centre within the School of Biological Sciences, School of Mathematics and Physics, School of Law and the Queensland Alliance for Agriculture and Food Innovation (QAAFI). The ARC CoE for Plant Success is administered at The University of Queensland and has Nodes at the University of Tasmania (UTAS), Monash University, Western Sydney University (WSU) and Queensland University of Technology (QUT). There are also a number of domestic and international partner institutions.

The ARC CoE for Plant Success will discover the adaptive strategies underpinning productivity and resilience in diverse plants and deepen knowledge of the genetic and physiological networks driving key traits. Using novel quantitative and computational approaches, the Centre will link gene networks with traits across biological levels, giving plant breeders an unparalleled predictive capacity. The Centre will accelerate technologies to transfer successful networks into crops and build legal frameworks to secure this knowledge. With a uniquely multidisciplinary team, the Centre will deliver new strategies to address the problems of food security and climate change, establishing Australia as a global leader in these areas.

Career Development

The Centre has an emphasis on early career development and there will be opportunities for formal career development and skills training as well as mentoring. Researchers will be embedded in a dynamic team and participate in a range of Centre related activities aimed at developing leadership skills.

Diversity and Inclusion

The Centre recognises and values equity and diversity, and encourages applications from any individual who meets the requirements of this position irrespective of gender, sexuality, race, ethnicity, religion, disability, age or other protected attributes. The Centre strives to provide an inclusive working environment, and along with the University is committed to supporting staff with family and caring responsibilities by providing policies, programs and initiatives to help balance work and family responsibilities.

Further information about the ARC CoE for Plant Success may be found at www.plantsuccess.org and should be explored to provide the context for this position which is primarily in the area of Phenotype Prediction; Predicting Phenotypes – Plant Success .

  

About You  

Level B

• Demonstrated experience in working with biological or other complex networks with an emphasis on quantitative analysis of populations and the integration of different layers of omics.

• Demonstrated ability to work in a cross-disciplinary environment and to conduct research independently and collaboratively.

• Evidence of, or an ability to commence establishing effective relationships to represent and promote the research area at a University and wider community level, including industry, government and professional bodies.

• High levels of personal integrity, transparency and capability.

• A strong track record of leading and working on multiple projects and producing timely, high quality outputs and outcomes. This could include contributions beyond their core research project and grant success.

• A record of publication in high-ranking refereed journals and other research outlets and

• A record of supervision of postgraduate research students.

• Demonstrated experience with software development and working with High-Performance Computing infrastructure.

Level C

In addition to the above, and relative to opportunity, the applicant will also be expected to have demonstrated clear evidence of independent leadership, such as being successful in several of the following:

• A demonstrated understanding of (i) directed networks, (ii) analysis and connectivity of networks across data layers including genomic, metabolomic, mechanistic and trait data, (iii) genomic prediction, (iv) machine learning.

• Experience in data visualization and super computing.

• A record of grant success.

• Demonstrated ability to work with organisations outside of academia such as CSIRO.

• A track record that includes invitations to present at conferences/workshops.

• An ability to supervise technicians and junior postdoctoral fellows.

• A record of innovation rather than simple application of established techniques.

• Advanced experience in software development for new applications and methods and working within a High-Performance Computing infrastructure.

What We Can Offer 

This is a full time, 100% FTE fixed-term position for up to 4 years at Academic Level B or C. The Level will be commensurate with the relevant skills and qualifications.

Academic Level B full-time equivalent base salary will be in the range $101,533 – $120,570 plus super of up to 17%. The total FTE package will be in the range $118,794 – $141,067 p.a.

Academic Level C full-time equivalent base salary will be in the range $124,378 – $143,415 plus super of up to 17%. The total FTE package will be in the range $145,523 – $167,796 p.a.

The following flexible employment options may be available for this role; some working from home; variable start or finish times; flex-time. 

For further information about UQ’s benefits, please visit Why Work at UQ and review The University of Queensland’s Enterprise Bargaining Agreement 2018 – 2021 . 

Questions? 

To discuss this role please contact Professor Christine Beveridge via [email protected]

For application queries, please contact [email protected] stating the job reference number in the subject line. 
 

Want to Apply? 

All applicants must upload the following documents in order for your application to be considered:

• Cover letter addressing the ‘About You’ section  

• Resume 

Please note that you will be asked to add all documents into the one upload box labelled ‘resume’, which is step one of the application form.

About the Selection Process 

Please note, that Interviews may be scheduled from November 25 2022 with the shortlisted candidates.

   
To satisfy pre-requisite questions and ensure your application can be considered in full, all candidates must apply via the UQ Careers portal by the job closing deadline or will not be accepted. 
 

Other Information 

Work Rights: You must have unrestricted work rights in Australia for the duration of this appointment to apply. Visa sponsorship is not available for this appointment.  

Background Checks: All final applicants for this position may be asked to consent to a criminal record check. Please note that people with criminal records are not automatically barred from applying for this position. Each application will be considered on its merits.  
 

We value diversity and inclusion, and actively encourage applications from those who bring diversity to the University. Our Diversity and Inclusion webpage contains further information if you require additional support. Accessibility requirements and/or adjustments can be directed to [email protected] . 

If you are a current employee of the University, or hold an unpaid or affiliate appointment with the University, please login to your staff Workday account and visit the internal careers board to apply for this opportunity. Please do NOT apply via the external job board.