Postdoctoral Research Fellow – Machine Learning and Software Engineer

As part of the UQ community, you will have the opportunity to work alongside the brightest minds, who have joined us from all over the world, and within an environment where interdisciplinary collaborations are encouraged.

At the core of our teaching remains our students, and their experience with us sets a foundation for success far beyond graduation. UQ has made a commitment to making education opportunities available for all Queenslanders, regardless of personal, financial, or geographical barriers.

As part of our commitment to excellence in research and professional practice in academic contexts, we are proud to provide our staff with access to world-class facilities and equipment, grant writing support, greater research funding opportunities, and other forms of staff support and development.

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, https://www.ideabio.org.au/). 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 https://www.qmap.org.au/) 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 specialising in Machine Learning to join a team of researchers to establish workflows to drive strain design and bioreactor operation optimisation at this facility. The position is primarily computationally based and will work with a metabolic modeller/data scientist, bioinformatician and dedicated Software Engineer. Modelling and machine-learning approaches will be used to learn from large data sets to identify optimal gene up/down regulation strategies to guide further strain engineering rounds, or to guide the optimisation of bioreactor operation. The role likely requires extensive coding and workflow development, and familiarity with Software Engineering principles or a Software Engineering background is extremely favourable.

Whilst the role is academic in nature, as an employee of an NCRIS-funded facility (https://www.education.gov.au/ncris), the position has a strong service focus. You will be required to aid in the analysis, interpretation and reporting of data to clients by contractual deadlines, which may include researchers and industry, whilst developing novel workflows to allow for high-throughput data analysis.

Key responsibilities will include: 

Research

• Develop data management pipelines and computational workflows to analyse and derive insights from small to moderate data-sets containing measurements of proteins, metabolites and bioreactor performance.
• Produce quality research outputs consistent with discipline norms by publishing or presenting in high quality outlets.
• Apply machine-learning approaches to maximise learning from smaller datasets.
• Expand or parameterise mechanistic models of metabolism or bioreactors with machine-learning approaches.
• 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

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.

Citizenship and Service

• Aid in the in-depth characterisation of bioreactor runs from data generated by the fermentation scientists and the reporting of results to clients.
• 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.

Other

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

This is a research focused position. Further information can be found by viewing UQ’s Criteria for Academic Performance.

This is a full-time (100%), fixed-term position for up to 12 months 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. As these roles are covered by an Enterprise Agreement, you will also receive regular remuneration increases in line with the Enterprise Agreement.

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.

• Completion or near completion of a PhD with demonstrated experience in Machine Learning
• A good-track record with data management
• Demonstrated familiarity or competency with Software Engineering best practices
• 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.

In addition, the following mandatory requirements apply:

• 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. 

For more information about this opportunity, please contact Dr Timothy McCubbin – **********@uq.edu.au  

For application queries, please contact ******@uq.edu.au stating the job reference number (below) in the subject line.
 

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

• Resume
• Cover letter
• Responses to the ‘About You’ section
   

At UQ we know that our greatest strengths come from our diverse mix of colleagues, this is reflected in our ongoing commitment to creating an environment focused on equity, diversity and inclusion.  We ensure that we are always attracting, retaining and promoting colleagues who are representative of the diversity in the broader community, whether that be gender identity, LGBTQIA+, cultural and/or linguistic, Aboriginal and/or Torres Strait Islander peoples, people with a disability, or age. Accessibility requirements and/or adjustments can be directed to ******@uq.edu.au.

If you are a current employee (including casual staff and HDR scholars) or hold an unpaid/affiliate appointment, 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.

Applications close 10 July 2024 at 11.00pm AEST (R-39392). Please note that interviews have been tentatively scheduled for July.