Postdoctoral Fellow in Machine Learning Applications for Utility-Scale PV
Postdoctoral Fellow in Machine Learning Applications for Utility-Scale PV
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Job Description
The Opportunity
The School of Photovoltaic and Renewable Energy Engineering (SPREE) has an opportunity for a Postdoctoral Fellow. You will contribute to UNSWs research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience in applying these methods to renewable energy (wind or photovoltaic) applications is highly desirable.
This position will provide you with the opportunity to develop your scholarly research and professional activities both nationally and internationally. You will contribute to writing scientific papers and reports for international journals participate in conferences and workshops supervise HDR students and actively engage with industry partners.
This role reports to Professor Ziv Hameiriand has no direct reports.
- Level A Salary - $110059 to $117718 per annum 17% superannuation
- Full time
- Fixed-term contract ASAP start until December 2025
- Location: Kensington Sydney Australia
About UNSW
UNSW isnt like other places youve worked. Yes were a large organisation with a diverse and talented community; a community doing extraordinary things. But what makes us different isnt only what we do its how we do it. Together we are driven to be thoughtful practical and purposeful in all we do. If you want a career where you can thrive be challenged and do meaningful work youre in the right place.
The School of Photovoltaic and Renewable Energy Engineering is internationally recognised for its record-breaking research in solar power (photovoltaics) and renewable energy. The PERC solar cell was first invented at UNSW in our labs in 1983 and today powers more than 85% of all new solar panel modules all over the world. SPREEs work and people have changed the face of sustainable energy on the global stage and we continue to be at the forefront of leading-edge research and development in the field of renewable technology as our economies transition away from fossil fuels. For more information please see the following link: & Experience:
- A PhD in Computer Science or a related field.
- Thorough theoretical background in machine learning and deep learning.
- Demonstrated experience in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data) preferably for renewable energy (wind or photovoltaic) applications.
- Proven expertise in (and/or proven ability to learn if necessary):
- One or more scientific programming languages such as Python (preferred) or R with a preference for functional style and algorithms experience.
- One or more deep learning frameworks such as PyTorch (preferred) or TensorFlow with a preference for experience implementing SOTA models and training procedures from academic journal papers.
- Development of data engineering pipelines (data aggregation and processing database management analysis and visualisation).
- Operating within Unix-based environments (headless servers HPC clusters) with a preference for experience managing server infrastructure.
- Collaborative software development using tools such as git/GitHub or equivalent.
- Highly desirable: experience with generative representation learning models (VAEs GANs etc.) or similar unsupervised data modelling techniques.
- A strong practice of maintaining an open-source code repository.
- A solid theoretical background in semiconductor device physics or substantial practical experience in electrical engineering especially related to photovoltaic devices and systems.
- Demonstrated research excellence. Evidence of winning major international awards for research is highly desirable.
- Demonstrated track record in research with outcomes of high quality and high impact.
- Proven commitment to proactively keeping up to date with discipline knowledge and developments.
- Demonstrated ability to work in a team collaborate across disciplines and build effective relationships.
- Demonstrated ability to communicate and interact with a diverse range of stakeholders and students.
- Evidence of highly developed interpersonal skills.
- An understanding of and commitment to UNSWs aims objectives and values in action together with relevant policies and guidelines.
- Knowledge of health and safety responsibilities and commitment to attending relevant health and safety training.
Additional details about the specific responsibilities for this position can be found in the position description. This is available via
To Apply: Please click the apply now button and submit your CV Cover Letter and Responses to the Skills and Experience. You should systematically address the Skills and Experience listed within the position description in your application.
Please note applications will not be accepted if sent to the contact listed below.
Contact:
Eugene Aves Talent Acquisition Consultant
Applications close: 11:55 pm (Sydney time) on Monday 16 June 2025
UNSW is committed to evolving a culture that embraces equity and supports a diverse and inclusive community where everyone can participate fairly in a safe and respectful environment. We welcome candidates from all backgrounds and encourage applications from people of diverse gender sexual orientation cultural and linguistic backgrounds Aboriginal and Torres Strait Islander background people with disability and those with caring and family responsibilities. UNSW provides workplace adjustments for people with disability and access to flexible work options for eligible staff. The University reserves the right not to proceed with any appointment.
Employment Type
Full-Time
