Postdoctoral Research Fellow - Risk Modelling & Maintenance Optimisation

Location: Mawson Lakes, SA, Australia, 5095

Employment Type: Fixed term - Full-time

Classification: Level A

Based at School of Computer Science and Information technology, Mawson Lakes campus

Work on cutting-edge energy reliability research

Full-time (1.0 FTE), fixed term contract until 30 June 2027

Salary Range: $102,024 to $109,307 per annum (plus 17% Superannuation)

Contribute to the development of dynamic risk and optimisation models that improve maintenance planning and asset reliability in industrial and energy sectors. Collaborate with researchers and industry partners to deliver practical, data-driven solutions for reliability and risk-informed decision-making.

At Adelaide University, we create the opportunities you need to achieve your ambitions – because when you thrive, we thrive.

We are transforming education for contemporary learners and global citizens. Building on a proud legacy and shaped by bold ambition, it’s a place of excellence and equity, where our vibrant community of staff are united by our purpose to inspire Australia’s future change-makers and create a better tomorrow.

Work that matters

This role contributes to the Future Energy Exports CRC project within the Industrial AI Research Centre by advancing risk-informed approaches to maintenance planning in industrial facilities. Your work will support the development of dynamic risk models and optimisation methods that improve asset reliability, particularly in environments with limited data.

You will help deliver scalable, practical solutions that enhance maintenance decision-making and reliability outcomes across large industrial organisations, including energy producers.

Curious to learn more? Explore the position description/selection criteria above to discover more about this opportunity.

The team

This role sits within a collaborative research team of the Industrial AI Research Centre working on the Future Energy Exports CRC project, focused on reliability engineering and risk optimisation. The team develops and applies dynamic risk models, integrating operational reliability data with maintenance planning processes.

Working closely with academic colleagues and industry partners, the team focuses on extending optimisation approaches, addressing data limitations, and implementing prototype solutions for piloting in real-world industrial environments.

Visit the AU website to learn more about this research area.

Our people

Contribute to the development of dynamic risk and optimisation models that improve maintenance planning and asset reliability in industrial and energy sectors. Collaborate with researchers and industry partners to deliver practical, data-driven solutions for reliability and risk-informed decision-making.

This role is ideal for someone who enjoys tackling complex challenges and working across both research and industry environments. You will engage with a variety of stakeholders and contribute to meaningful, applied outcomes.

Learn more about our people, what we stand for and what we offer at Careers at AU.

Experience

To thrive in this role, you will likely have the following skills and experience:

A recent PhD graduate in computer science, engineering and supply networks, artificial intelligence, machine learning, or a closely related field.

Knowledge of and experience with Mixed Integer Linear Programming optimisation, machine learning, or similar methods.

Experience with implementing AI, data science, and/or machine learning solutions in Python.

Proven ability to analyse complex systems and develop practical solutions to challenges such as limited or sparse data availability.

Strong problem-solving skills, with the ability to extend and adapt existing methodologies (such as optimisation approaches) to new or large-scale applications.

Experience in implementing or prototyping technical solutions, with an understanding of how to translate research outcomes into real-world industry environments.

Our commitment to inclusion and diversity

We are committed to fostering a culture of inclusion where diversity is celebrated and everyone feels respected and valued. Adelaide University is an equal opportunity employer, committed to creating a safe, inclusive, and equitable workplace where everyone can thrive. We strongly encourage applications from Aboriginal and Torres Strait Islander peoples, people with disability, and people of all ages, genders, cultural backgrounds, sexual orientations, and gender identities. We are committed to supporting flexible working arrangements and providing reasonable adjustments throughout the recruitment process.

Build on your career with Adelaide University now

Simply click on the Apply Now button and upload:

your current CV

a cover letter that tells us why you’re excited about the role

The online application form will list the specific selection criteria that you need to address.

Submit your application by 11:30pm Friday 17 July 2026

For further information about this opportunity, please contact (quoting reference number 492778):

Simone Patterson

Talent Acquisition Adviser

+61 8 8302 1700 | careers.adelaideuniversity@adelaide.edu.au

Applications welcomed from Australian or NZ citizens, Australian permanent residents and those who have the legal right to work in Australia for the term of appointment.

Postdoctoral Research Fellow - Risk Modelling & Maintenance Optimisation

Location: Mawson Lakes, SA, Australia, 5095

Employment Type: Fixed term - Full-time

Classification: Level A

Based at School of Computer Science and Information technology, Mawson Lakes campus

Work on cutting-edge energy reliability research

Full-time (1.0 FTE), fixed term contract until 30 June 2027

Salary Range: $102,024 to $109,307 per annum (plus 17% Superannuation)

Contribute to the development of dynamic risk and optimisation models that improve maintenance planning and asset reliability in industrial and energy sectors. Collaborate with researchers and industry partners to deliver practical, data-driven solutions for reliability and risk-informed decision-making.

At Adelaide University, we create the opportunities you need to achieve your ambitions – because when you thrive, we thrive.

We are transforming education for contemporary learners and global citizens. Building on a proud legacy and shaped by bold ambition, it’s a place of excellence and equity, where our vibrant community of staff are united by our purpose to inspire Australia’s future change-makers and create a better tomorrow.

Work that matters

This role contributes to the Future Energy Exports CRC project within the Industrial AI Research Centre by advancing risk-informed approaches to maintenance planning in industrial facilities. Your work will support the development of dynamic risk models and optimisation methods that improve asset reliability, particularly in environments with limited data.

You will help deliver scalable, practical solutions that enhance maintenance decision-making and reliability outcomes across large industrial organisations, including energy producers.

Curious to learn more? Explore the position description/selection criteria above to discover more about this opportunity.

The team

This role sits within a collaborative research team of the Industrial AI Research Centre working on the Future Energy Exports CRC project, focused on reliability engineering and risk optimisation. The team develops and applies dynamic risk models, integrating operational reliability data with maintenance planning processes.

Working closely with academic colleagues and industry partners, the team focuses on extending optimisation approaches, addressing data limitations, and implementing prototype solutions for piloting in real-world industrial environments.

Visit the AU website to learn more about this research area.

Our people

Contribute to the development of dynamic risk and optimisation models that improve maintenance planning and asset reliability in industrial and energy sectors. Collaborate with researchers and industry partners to deliver practical, data-driven solutions for reliability and risk-informed decision-making.

This role is ideal for someone who enjoys tackling complex challenges and working across both research and industry environments. You will engage with a variety of stakeholders and contribute to meaningful, applied outcomes.

Learn more about our people, what we stand for and what we offer at Careers at AU.

Experience

To thrive in this role, you will likely have the following skills and experience:

A recent PhD graduate in computer science, engineering and supply networks, artificial intelligence, machine learning, or a closely related field.

Knowledge of and experience with Mixed Integer Linear Programming optimisation, machine learning, or similar methods.

Experience with implementing AI, data science, and/or machine learning solutions in Python.

Proven ability to analyse complex systems and develop practical solutions to challenges such as limited or sparse data availability.

Strong problem-solving skills, with the ability to extend and adapt existing methodologies (such as optimisation approaches) to new or large-scale applications.

Experience in implementing or prototyping technical solutions, with an understanding of how to translate research outcomes into real-world industry environments.

Our commitment to inclusion and diversity

We are committed to fostering a culture of inclusion where diversity is celebrated and everyone feels respected and valued. Adelaide University is an equal opportunity employer, committed to creating a safe, inclusive, and equitable workplace where everyone can thrive. We strongly encourage applications from Aboriginal and Torres Strait Islander peoples, people with disability, and people of all ages, genders, cultural backgrounds, sexual orientations, and gender identities. We are committed to supporting flexible working arrangements and providing reasonable adjustments throughout the recruitment process.

Build on your career with Adelaide University now

Simply click on the Apply Now button and upload:

your current CV

a cover letter that tells us why you’re excited about the role

The online application form will list the specific selection criteria that you need to address.

Submit your application by 11:30pm Friday 17 July 2026

For further information about this opportunity, please contact (quoting reference number 492778):

Simone Patterson

Talent Acquisition Adviser

+61 8 8302 1700 | careers.adelaideuniversity@adelaide.edu.au

Applications welcomed from Australian or NZ citizens, Australian permanent residents and those who have the legal right to work in Australia for the term of appointment.