Application
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Postdoctoral researcher
Postdoctoral Fellow in AI and Machine Learning for Photovoltaics
UNSW Sydney
Develop and apply artificial intelligence, machine learning and data-driven methods for emerging thin-film and tandem photovoltaic technologies, including predictive and physics-informed modelling and analysis of experimental and operational datasets.
The application window for this cycle has ended. The programme page tracks the next cycle.
This application cycle is archived.
Employment
Vacancy terms
- Career stage
- Postdoctoral researcher
- Employment type
- Not stated
- Contract
- Not stated
- Contract ends
- Not stated
- Original contract term
- 1 year, with possible extension for another 4 years
- Schedule
- Full-time, 35 hours per week
- Workplace
- Not stated
- FTE
- 1
- Salary
- AUD 118,467 to 126,711 per annum plus 17% superannuation
- Department or school
- School of Photovoltaic and Renewable Energy Engineering
- Posted
- 27 Jul 2026
Role
Responsibilities
- Develop predictive and physics-informed models.
- Analyse experimental and operational datasets.
- Contribute to multidisciplinary research.
- Disseminate research outcomes.
- Participate in conferences and workshops.
- Assist with research-student supervision.
Candidate profile
Qualifications and requirements
Qualifications
- PhD or postdoctoral research experience in artificial intelligence, machine learning, data science, physics, engineering, materials science or a closely related discipline, and/or relevant work experience.
- Ability to undertake high-quality academic research independently with limited supervision.
- Publication and conference-presentation record relative to opportunity.
- Ability to collaborate across disciplines and communicate with diverse stakeholders and students.
Requirements
- Relevant research experience in AI, machine learning, data science, physics, engineering or materials science.
- Predictive modelling
- Machine learning
- Physics-informed modelling
- Digital-twin development
- Reliability analysis
- Materials informatics
- Experimental and operational data analysis
Employment package
Benefits
- AUD 118,467 to 126,711 per annum plus 17% superannuation
- 17% superannuation. Access to flexible work options for eligible staff is stated. Relocation and visa support are not stated.
- 1 year, with possible extension for another 4 years
Application
Documents and steps
Documents
- Cover letter
- CV
- Responses addressing the stated Skills and Experience
Steps
Apply online through the vacancy-specific UNSW PageUp application gateway.
Submit through the verified official application route before the stated deadline.
Official evidence
Why this vacancy is published
This vacancy was checked against the university's official careers system.
Compensation And Contract
Official vacancy page states AUD 118,467 to 126,711 per annum plus 17% superannuation, Level A, full-time employment and a 1 year, with possible extension for another 4 years fixed-term contract.UNSW Sydney
Deadline
Official vacancy page states the closing date 2026-08-26 at 23:55 Sydney time, AUS Eastern Standard Time.UNSW Sydney
Identity And Status
Official UNSW vacancy page identifies job 541338, the title, full-time work type and Post Doctoral Research Associate category.UNSW Sydney
Qualifications And Requirements
PhD or postdoctoral research experience in artificial intelligence, machine learning, data science, physics, engineering, materials science or a closely related discipline, and/or relevant work experience. Relevant research experience in AI, machine learning, data science, physics, engineering or materials science. Ability to undertake high-quality academic research independently with limited supervision. Publication and conference-presentation record relative to opportunity. Ability to collaborate across disciplines and communicate with diverse stakeholders and students.UNSW Sydney
Change history
Published revision history
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Programme continuity
Postdoctoral Fellow in AI and Machine Learning for Photovoltaics
Use the enduring programme page to check for a verified newer cycle. A closed vacancy never implies that another round is open.
Programme overview and cycle history