Synthetic biology is characterised by a cyclic workflow based on the design, build, test, learn paradigm. The emergence of genome-foundries allows for the rapid, but still costly, generation of large numbers of engineered strains. The University of Queensland is establishing a complementary facility to genome-foundries, the Integrated Design Environment for Advanced biomanufacturing (IDEA bio, This facility will consist of a TEST capability that allows for a deep phenomic characterisation of mutant strains, in partnership with Queensland Metabolomics and Proteomics (Q MAP and the Australian Genome Foundry (Macquarie University), as well as a LEARN capability that seeks to learn from large ‘omics data sets and direct strain optimisation, pathway optimisation and metabolic engineering to advance synthetic biology efforts in Australia.

We are seeking a Postdoctoral Research Fellow that specialises in machine learning to join a team of researchers to establish workflows to drive strain design capability of this facility. The position is primarily computationally based and will work with a metabolic modeller/data scientist to use modelling and machine-learning approaches to learn from large data sets to identify optimal gene up/down regulation strategies to guide further strain engineering rounds. Successful candidates will work with customers of the facility which may include researchers and industry.

 Key responsibilities include:


1. Research

  • Produce quality research outputs consistent with discipline norms by publishing or presenting in high quality outlets.
  • Work with colleagues in the development of joint research projects and applications for competitive research funding support.
  • Contribute to progressing towards transfer of knowledge, technology and practices to research end users through translation, including commercialisation of UQ intellectual property.
  • Develop a coherent research program and an emerging research profile.
  • Review and draw upon best practice research methodologies

2. Supervision and Researcher Development

  • Contribute to the effective supervision of Honours and Higher Degree by Research students (as appropriate).
  • Demonstrates personal effectiveness in supervision and the management of researcher development.
  • Effective lead and develop supervisee performance and conduct by providing feedback, coaching, and professional development.
  • As appropriate, manage research support staff effectively throughout the employee lifecycle in accordance with University policy and procedures.
  • Working to promptly resolve conflict and grievances when they arise in accordance with University policy and procedures.

3. Citizenship and Service

  • Demonstrate citizenship behaviours that align to the UQ values.
  • Shows leadership of self through collaboration and active participation in priority activities for the unit
  • 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.
  • Begin to develop external links and partnerships by cultivating relationships with industry, government departments, professional bodies and the wider community.

4. Other

Ensure you are aware of and comply with legislation and University policy relevant to the duties undertaken, including but not exclusive to:

About You

  • Completion or near completion of a PhD with demonstrated experience in machine learning
  • A good-track record with data management
  • Demonstrated knowledge and application of various machine-learning approaches
  • Evidence of publications in peer-reviewed journals
  • Well-versed in at least one programming language/environment (such as Python, MATLAB or similar)
  • Good organisational and problem-solving skills.
  • Proven ability to work collaboratively as part of a multi-disciplinary team.
  • Strong interpersonal and communication skills.
  • Familiarity with metabolism and the analysis of ‘omics data sets such as metabolomics and proteomics is favourable, but not essential.

What We Can Offer  

This is a full time, fixed term position to 31 March 2024 at Academic Level A.

The full-time equivalent base salary will be in the range $72,144.35 – $96,530.67 plus super of up to 17%. The total FTE package will be in the range $84,408.89 – 112,940.88 per annum.  

The following flexible employment options may be available for this role:  ​Part time/job share; some working from home; variable start or finish times; compressed hours; purchased leave; 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.  


To discuss this role please contact Dr Timothy McCubbin, Senior Data Scientist and Team Leader: .

For application queries, please contact stating the job reference number in the subject line.  

Want to Apply?  

All applicants must supply the following documents:  

  • Cover letter addressing ‘About You’ section above   
  • Resume  

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. Applications received via other channels, including direct email, will not be accepted.  

About The Selection Process  

The University of Queensland is committed to ensuring all candidates are provided with the opportunity to attend the panel interviews, however, for those candidates who are unable to attend in person, video interview options will be available.     

Other Information 

Work Rights: Visa sponsorship maybe available, however Australian on shore and New Zealand applicants will be prioritised.

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

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.


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