About This Opportunity Multiple opportunities are available for Postdoctoral Research Fellow/Research Fellow (Level A or Level B) with expertise in Computational and Statistical Genetics, Quantitative Genetics, Cellular Genomics, or more generally in Bayesian Statistics to engage in cutting-edge genomics research, further their knowledge and grow their profile nationally and internationally. Position 1 – Integrating complex trait genomics with single-cell omics (Level A). This research aims to develop new statistical and machine learning methods to integrate and analyse data from genome-wide association studies (GWAS) and single-cell multi-omics technologies, including those with spatial resolution. Methods developed from this project will be applied to diverse datasets to understand the cellular biology underlying complex trait variation, help fine map the genetic variants and genes with causal effects, and improve polygenic risk prediction for common diseases. This role will be supervised by Dr Jian Zeng. Position 2 – Polygenic prediction using whole-genome sequencing data (Level A). This project will focus on developing innovative Bayesian methods to leverage whole-genome sequencing data and various functional genomic annotations for polygenic prediction. These methods will be applied to a range of complex traits and diseases using hundreds of thousands of whole-genome sequences from large biobanks and consortia in diverse human populations. The outcome of the project will advance genomic risk prediction for individuals across different ancestral backgrounds. This role will be supervised by Dr Jian Zeng. Position 3 – Genetic variation and prediction in admixed populations (Level B). This position would be suited for an experienced researcher (ideally with prior postdoctoral experience) who aims to transition into starting their own laboratory in the next 5 years. The statistical genomics laboratory will provide mentorship and opportunities to lead to help the selected candidate establish themselves as a future leader in the field of statistical genetics. The selected candidate will be leading a series of projects focusing on developing and applying statistical methods for identifying causal genetic variations for multiple complex traits and diseases and use that knowledge to improve phenotype prediction in genetically diverse populations. Beyond frameworks currently used in statistical genetics (e.g., Bayesian regression models), these projects will leverage DNA foundational models to expand the set of genomic annotation used for phenotype prediction. This role will report to Professor Loic Yengo - https://www.snowmedical.org.au/our-snow-fellows/loic-yengo. Key responsibilities will include: Research: Academic Level A Generate new hypotheses and run statistical analyses implementing existing methods (e.g., GWAS, cell type mapping, Bayesian methods). Participate in the development of novel approaches to analyse large scale genomic and single-cell omic data (e.g., by deriving mathematical results underlying the methods, by running simulation studies to assess the statistical properties of the methods) Produce quality research outputs (e.g., publications in peer-reviewed scientific journals, presentation at conference). Develop a coherent research program and an emerging research profile. Academic Level B Duties listed above, in addition to the following: Take on a leadership role in the development of novel approaches to analyse large scale genomic data Contribute to the implementation and dissemination of software tools developed to run new analyses Work with colleagues towards 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. Supervision and Researcher Development: Academic Level A Contribute to the effective supervision of Honours and Higher Degree by Research students. Demonstrates personal effectiveness in supervision and the management of researcher development. Academic Level B Contribute to the effective supervision of Honours and Higher Degree by Research students. Demonstrate a track record of the effective supervision of Honours and Higher Degree by Research students. Demonstrates personal effectiveness in supervision and management and development of researcher capability and skill. Effective lead and develop supervisee performance and conduct by providing feedback, coaching, and professional development. Manage 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: Develop partnerships, demonstrate leadership through mentoring, engage in internal service roles and committees, perform administrative functions, provide support to colleagues, and uphold university values. This is a research focused position. Further information can be found by viewing UQ’s Criteria for Academic Performance. About UQ 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. 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. About You Completion of a PhD in Statistical Genetics, Computational Genetics, Cellular Genomics, Quantitative Genetics, Statistics or other relevant areas. Knowledge or expertise in the principles of genetics and genomics, Strong background in statistics or data science Expertise in linear algebra and strong knowledge of the linear model methodologies Proven ability to code efficiently in low-level programming languages (e.g., C/C++). Evidence of research productivity, including high-profile publications, conference presentations and external grant applications. Demonstrated high-level communication and interpersonal skills including the ability to effectively collaborate to ensure research aims are met. Ability to work independently with excellent problem solving and organisational skills, with high attention to detail Knowledge or Expertise in one or more of the following is desirable: Quantitative genetics models and theories Development and application of multi-marker methods, especially Bayesian methods Analysis of large-scale SNP array and whole genome sequencing data Analysis of cellular transcriptomic or epigenomic data Machine learning methods Integrative analysis of omics data Academic Level B Evidence of successfully seeking, obtaining and managing external research funding. A growing record of supervision of Honours and Research Higher Degree students to successful completion. The successful candidate may be required to complete a number of pre-employment checks, including: right to work in Australia, education check. Relocating from interstate or overseas? We may support you with obtaining employer-sponsored work rights and a relocation support package. You can find out more about life in Australia’s Sunshine State here. Questions? For more information about this opportunity, please contact Dr Jian Zeng j.zeng@imb.uq.edu.au and Professor Loic Yengo l.yengo@imb.uq.edu.au. For application queries, please contact talent@uq.edu.au stating the job reference number (below) in the subject line. Want to Apply? All applicants must upload the following documents in order for your application to be considered: Resume Cover letter *please specify the position/s you’d be most interested in Responses to the ‘About You’ section Other Information UQ is committed to a fair, equitable and inclusive selection process, which recognises that some applicants may face additional barriers and challenges which have impacted and/or continue to impact their career trajectory. Candidates who don’t meet all criteria are encouraged to apply and demonstrate their potential. The selection panel considers both potential and performance relative to opportunities when assessing suitability for the role. We know one of our strengths as an institution lies in our diverse colleagues. We're dedicated to equity, diversity, and inclusion, fostering an environment that mirrors our wider community. We're committed to attracting, retaining, and promoting diverse talent. Reach out to talent@uq.edu.au for accessibility support or adjustments. Applications close Friday 28 February 2025 at 11.00pm AEST (R-46871). If you apply for this position please say you saw it on Academicoxy

About This Opportunity

Multiple opportunities are available for Postdoctoral Research Fellow/Research Fellow (Level A or Level B) with expertise in Computational and Statistical Genetics, Quantitative Genetics, Cellular Genomics, or more generally in Bayesian Statistics to engage in cutting-edge genomics research, further their knowledge and grow their profile nationally and internationally.

Position 1 – Integrating complex trait genomics with single-cell omics (Level A). This research aims to develop new statistical and machine learning methods to integrate and analyse data from genome-wide association studies (GWAS) and single-cell multi-omics technologies, including those with spatial resolution. Methods developed from this project will be applied to diverse datasets to understand the cellular biology underlying complex trait variation, help fine map the genetic variants and genes with causal effects, and improve polygenic risk prediction for common diseases. This role will be supervised by Dr Jian Zeng.

Position 2 – Polygenic prediction using whole-genome sequencing data (Level A). This project will focus on developing innovative Bayesian methods to leverage whole-genome sequencing data and various functional genomic annotations for polygenic prediction. These methods will be applied to a range of complex traits and diseases using hundreds of thousands of whole-genome sequences from large biobanks and consortia in diverse human populations. The outcome of the project will advance genomic risk prediction for individuals across different ancestral backgrounds. This role will be supervised by Dr Jian Zeng.

Position 3 – Genetic variation and prediction in admixed populations (Level B). This position would be suited for an experienced researcher (ideally with prior postdoctoral experience) who aims to transition into starting their own laboratory in the next 5 years. The statistical genomics laboratory will provide mentorship and opportunities to lead to help the selected candidate establish themselves as a future leader in the field of statistical genetics. The selected candidate will be leading a series of projects focusing on developing and applying statistical methods for identifying causal genetic variations for multiple complex traits and diseases and use that knowledge to improve phenotype prediction in genetically diverse populations. Beyond frameworks currently used in statistical genetics (e.g., Bayesian regression models), these projects will leverage DNA foundational models to expand the set of genomic annotation used for phenotype prediction. This role will report to Professor Loic Yengo - https://www.snowmedical.org.au/our-snow-fellows/loic-yengo.

Key responsibilities will include:

Research:

Academic Level A

Generate new hypotheses and run statistical analyses implementing existing methods (e.g., GWAS, cell type mapping, Bayesian methods).
Participate in the development of novel approaches to analyse large scale genomic and single-cell omic data (e.g., by deriving mathematical results underlying the methods, by running simulation studies to assess the statistical properties of the methods)
Produce quality research outputs (e.g., publications in peer-reviewed scientific journals, presentation at conference).
Develop a coherent research program and an emerging research profile.

Academic Level B

Duties listed above, in addition to the following:

Take on a leadership role in the development of novel approaches to analyse large scale genomic data
Contribute to the implementation and dissemination of software tools developed to run new analyses
Work with colleagues towards 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.

Supervision and Researcher Development:

Academic Level A

Contribute to the effective supervision of Honours and Higher Degree by Research students.
Demonstrates personal effectiveness in supervision and the management of researcher development.

Academic Level B

Contribute to the effective supervision of Honours and Higher Degree by Research students.
Demonstrate a track record of the effective supervision of Honours and Higher Degree by Research students.
Demonstrates personal effectiveness in supervision and management and development of researcher capability and skill.
Effective lead and develop supervisee performance and conduct by providing feedback, coaching, and professional development.
Manage 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: Develop partnerships, demonstrate leadership through mentoring, engage in internal service roles and committees, perform administrative functions, provide support to colleagues, and uphold university values.

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

About UQ

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.

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.

About You

Completion of a PhD in Statistical Genetics, Computational Genetics, Cellular Genomics, Quantitative Genetics, Statistics or other relevant areas.
Knowledge or expertise in the principles of genetics and genomics,
Strong background in statistics or data science
Expertise in linear algebra and strong knowledge of the linear model methodologies
Proven ability to code efficiently in low-level programming languages (e.g., C/C++).
Evidence of research productivity, including high-profile publications, conference presentations and external grant applications.
Demonstrated high-level communication and interpersonal skills including the ability to effectively collaborate to ensure research aims are met.
Ability to work independently with excellent problem solving and organisational skills, with high attention to detail
Knowledge or Expertise in one or more of the following is desirable:
Quantitative genetics models and theories
Development and application of multi-marker methods, especially Bayesian methods
Analysis of large-scale SNP array and whole genome sequencing data
Analysis of cellular transcriptomic or epigenomic data
Machine learning methods
Integrative analysis of omics data

Academic Level B

Evidence of successfully seeking, obtaining and managing external research funding.
A growing record of supervision of Honours and Research Higher Degree students to successful completion.

The successful candidate may be required to complete a number of pre-employment checks, including: right to work in Australia, education check.

Relocating from interstate or overseas? We may support you with obtaining employer-sponsored work rights and a relocation support package. You can find out more about life in Australia’s Sunshine State here.

Questions?

For more information about this opportunity, please contact Dr Jian Zeng j.zeng@imb.uq.edu.au and Professor Loic Yengo l.yengo@imb.uq.edu.au. For application queries, please contact talent@uq.edu.au stating the job reference number (below) in the subject line.

Want to Apply?

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

Resume
Cover letter *please specify the position/s you’d be most interested in
Responses to the ‘About You’ section

Other Information

UQ is committed to a fair, equitable and inclusive selection process, which recognises that some applicants may face additional barriers and challenges which have impacted and/or continue to impact their career trajectory. Candidates who don’t meet all criteria are encouraged to apply and demonstrate their potential. The selection panel considers both potential and performance relative to opportunities when assessing suitability for the role.

We know one of our strengths as an institution lies in our diverse colleagues. We're dedicated to equity, diversity, and inclusion, fostering an environment that mirrors our wider community. We're committed to attracting, retaining, and promoting diverse talent. Reach out to talent@uq.edu.au for accessibility support or adjustments.

Applications close Friday 28 February 2025 at 11.00pm AEST (R-46871).

If you apply for this position please say you saw it on Academicoxy

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