Scientist in Computer Science (University of Queensland)

Full-time (100%), fixed-term position for up to 2 years Base salary: $114,824.05 – $136,048.72 + 17% Superannuation (Academic Level B) Location: St Lucia Campus About This Opportunity We’re looking for a talented Spatial/Geospatial Scientist to drive innovation in drone-based data pipelines and analysis. You’ll collaborate with leading e-research partners, access world-class computing resources, and develop cutting-edge solutions in remote sensing and spatial data. Reporting to the Drone CRP Director, you’ll work closely with the Data Analytics Collaborative Research Platform. This role offers opportunities for research leadership, HDR supervision, and involvement in major funding applications. If you’re passionate about spatial data and creating impact, join us! Key Responsibilities 1. Data Pipeline and Management Work with the RCC to ensure data and metadata can be reliably and automatically parsed, indexed, stored and extracted, for a variety of remote sensing data types (multispectral, hyperspectral, and LiDAR data). Automate data ingestion, processing, and delivery workflows, including data versioning and quality control protocols to ensure the integrity and traceability of all datasets. Ensure all data assets (raw, processed, and derived) are Findable, Accessible, Interoperable, and Reusable (FAIR), and that a comprehensive metadata schema is followed, consistent with relevant industry standards (e.g., ISO 19115). 2. Geospatial Analysis and Machine Learning Develop and implement analytical tools and routines to extract valuable insights from remote sensing data. These may include machine learning techniques for tasks such as object detection, change detection and classification, or the calculation of custom band ratios (e.g., NDVI, NDRE) and other spectral indices to support research and industry projects. Advise and collaborate with researchers and external partners on the most effective analytical methods for their projects. 3. Collaboration and Reporting Work closely with the Centre Director, drone pilots, and researchers to understand data requirements and deliver tailored solutions. Prepare technical documentation, reports, and presentations to communicate findings, methodologies, and data management procedures. Stay updated on emerging technologies and industry best practices. Provide support during field campaigns and be willing to travel to conferences and stakeholder meetings to represent the CRP or present project results. Key Responsibilities for Research Focused (Platform) Academics at Level B Include Progressing towards transfer of knowledge, technology and practices to research end-users through translation, including commercialisation. Development of, or delivery of partnerships with research end-users, or external collaborations resulting in quality outputs. Successfully builds teams and participates in discipline service. Demonstrates and leads others in responsible conduct of research. Development of supervisee capabilities and skills. Shows leadership in facilitating engagement opportunities for supervisees. Effective in management of the development of others, and development supervision capabilities. This is a research-focused, platform academic 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, access to exclusive internal-only vacancies, and genuine career progression opportunities via the academic promotions process. About You A postgraduate qualification (Masters or PhD) in a relevant field such as Data Science, Geospatial Data Science, Remote Sensing, Computer Science, or Software Engineering, or an equivalent combination of education and professional experience. Experience in designing and implementing data pipelines and data storage solutions, preferably for large-scale geospatial or remote sensing datasets. Demonstrated knowledge of and experience in applying FAIR data principles and managing metadata. Proficiency in programming languages such as Python or C++, with experience using relevant libraries (e.g., GDAL, Rasterio, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch). Strong understanding of geospatial data formats and concepts, including projections and coordinate systems. Experience with machine learning and other geostatistical techniques applied to imagery or geospatial data. Excellent problem-solving, analytical, and communication skills. The successful candidate may be required to complete a number of pre-employment checks, including right to work in Australia and education check. Relocating from interstate or overseas? Support may be provided with employer-sponsored work rights and a relocation package. Questions? For more information about this opportunity, please contact Associate Professor Steven Micklethwaite at s.micklethwaite@uq.edu.au. For application inquiries, please email the Talent Acquisition team at talent@uq.edu.au and include the job reference number below in the subject line. Want to Apply? To be considered, please upload: Resume Cover letter responding to the ‘About You’ section. UQ is committed to equity, diversity and inclusion. If you require an alternative application method due to accessibility needs or personal circumstances, contact talent@uq.edu.au. Other Information UQ is committed to a fair, equitable and inclusive selection process. 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. Applications close Thursday 15 January 2026 at 11:00pm AEST (R-57941). Interviews are tentatively scheduled for late January. If you apply for this position please say you saw it on Computeroxy

Full-time (100%), fixed-term position for up to 2 years
Base salary: $114,824.05 – $136,048.72 + 17% Superannuation (Academic Level B)
Location: St Lucia Campus

About This Opportunity

We’re looking for a talented Spatial/Geospatial Scientist to drive innovation in drone-based data pipelines and analysis. You’ll collaborate with leading e-research partners, access world-class computing resources, and develop cutting-edge solutions in remote sensing and spatial data.

Reporting to the Drone CRP Director, you’ll work closely with the Data Analytics Collaborative Research Platform. This role offers opportunities for research leadership, HDR supervision, and involvement in major funding applications. If you’re passionate about spatial data and creating impact, join us!

Key Responsibilities

1. Data Pipeline and Management

Work with the RCC to ensure data and metadata can be reliably and automatically parsed, indexed, stored and extracted, for a variety of remote sensing data types (multispectral, hyperspectral, and LiDAR data).
Automate data ingestion, processing, and delivery workflows, including data versioning and quality control protocols to ensure the integrity and traceability of all datasets.
Ensure all data assets (raw, processed, and derived) are Findable, Accessible, Interoperable, and Reusable (FAIR), and that a comprehensive metadata schema is followed, consistent with relevant industry standards (e.g., ISO 19115).

2. Geospatial Analysis and Machine Learning

Develop and implement analytical tools and routines to extract valuable insights from remote sensing data. These may include machine learning techniques for tasks such as object detection, change detection and classification, or the calculation of custom band ratios (e.g., NDVI, NDRE) and other spectral indices to support research and industry projects.
Advise and collaborate with researchers and external partners on the most effective analytical methods for their projects.

3. Collaboration and Reporting

Work closely with the Centre Director, drone pilots, and researchers to understand data requirements and deliver tailored solutions.
Prepare technical documentation, reports, and presentations to communicate findings, methodologies, and data management procedures.
Stay updated on emerging technologies and industry best practices.
Provide support during field campaigns and be willing to travel to conferences and stakeholder meetings to represent the CRP or present project results.

Key Responsibilities for Research Focused (Platform) Academics at Level B Include

Progressing towards transfer of knowledge, technology and practices to research end-users through translation, including commercialisation.
Development of, or delivery of partnerships with research end-users, or external collaborations resulting in quality outputs.
Successfully builds teams and participates in discipline service.
Demonstrates and leads others in responsible conduct of research.
Development of supervisee capabilities and skills.
Shows leadership in facilitating engagement opportunities for supervisees.
Effective in management of the development of others, and development supervision capabilities.

This is a research-focused, platform academic 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, access to exclusive internal-only vacancies, and genuine career progression opportunities via the academic promotions process.

About You

A postgraduate qualification (Masters or PhD) in a relevant field such as Data Science, Geospatial Data Science, Remote Sensing, Computer Science, or Software Engineering, or an equivalent combination of education and professional experience.
Experience in designing and implementing data pipelines and data storage solutions, preferably for large-scale geospatial or remote sensing datasets.
Demonstrated knowledge of and experience in applying FAIR data principles and managing metadata.
Proficiency in programming languages such as Python or C++, with experience using relevant libraries (e.g., GDAL, Rasterio, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
Strong understanding of geospatial data formats and concepts, including projections and coordinate systems.
Experience with machine learning and other geostatistical techniques applied to imagery or geospatial data.
Excellent problem-solving, analytical, and communication skills.

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

Relocating from interstate or overseas? Support may be provided with employer-sponsored work rights and a relocation package.

Questions?

For more information about this opportunity, please contact Associate Professor Steven Micklethwaite at s.micklethwaite@uq.edu.au.

For application inquiries, please email the Talent Acquisition team at talent@uq.edu.au and include the job reference number below in the subject line.

Want to Apply?

To be considered, please upload:

Resume
Cover letter responding to the ‘About You’ section.

UQ is committed to equity, diversity and inclusion. If you require an alternative application method due to accessibility needs or personal circumstances, contact talent@uq.edu.au.

Other Information

UQ is committed to a fair, equitable and inclusive selection process. 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.

Applications close Thursday 15 January 2026 at 11:00pm AEST (R-57941).
Interviews are tentatively scheduled for late January.

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

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