Equity and Diversity at UNSW:
“UNSW was one of the first cohort of research and higher education institutions in Australia to receive Bronze Athena Swan accreditation in 2018. Using the Athena Swan framework, the University built an action plan designed to overcome barriers for women specific to the institution around promotions, flexible work, career development and workplace culture among others.” 
The ACDC team was also awarded the Engineering Faculty excellence award for our contribution to Equity, Diversity and Inclusion. Aligning with our values, we strongly encourage diverse applications and will prioritise female applicants for this PhD position.
The continuous increase in energy conversion efficiency and decrease in the cost have made solar power the cheapest form of electricity in most countries. As a result, silicon-based solar cell technologies are currently dominating the global photovoltaic (PV) market. Furthermore, with the global push to combat climate change, it's estimated that over 80 TW of solar power will need to be installed by 2050 - which is a huge increase from the current global cumulative capacity of 1 TW achieved in 2022. Such growth in the PV industry was previously driven by economies of scale. To facilitate and continue this immense growth, the industry will need the assistance of artificial intelligence in all aspects of the PV supply chain.
Characterisation plays a vital role in the development and monitoring of solar cells in all aspects from manufacturing of the wafers to a PV module’s end-of-life. Utilising machine learning (ML) to unlock powerful characterisation techniques as well as making current techniques more effective will enhance the development and monitoring of PV cells and modules. We are seeking a motivated PhD candidates to develop state-of-the-art ML applications for different aspects of the PV value chain. You will join a strong team of talented researchers working to apply ML to:
- extract various electrical properties for luminescence images of silicon wafers, solar cells, and PV modules
- improve the reliability of manufacturing lines and installed PV systems
- develop the new generation of solar cells
- automate the decision making process for end-of-life processing of PV modules
In this PhD project, you'll dive into advanced characterisation techniques like luminescence imaging and outdoor imaging systems. Plus, you'll innovate by adapting existing ML and deep learning models to advance other existing characterisation techniques. And with access to powerful computational resources, state-of-the-art labs, and rich datasets for training ML models, you'll have everything you need to conduct cutting-edge research and make significant contributions to the field of solar cell characterisation.
- Undergraduate Degree: Bachelor’s degree in Electrical Engineering, Electronics, Physics, or Applied Science with a graduation GPA above 8 out of 10 or equivalent.
- Master degree: Priority will be given to those who graduated from a Masters by research program, focusing on photovoltaic devices or similar.
About the Team:
The School of Photovoltaic and Renewable Energy Engineering (SPREE) is one of the eight schools within the Faculty of Engineering at the University of New South Wales (UNSW), Sydney, Australia. The school is widely considered the best in the world. Building on its ground-breaking research, the school attracts leading international researchers in the area of photovoltaics. Our academic staff has been consistently ranked amongst the leaders worldwide in the photovoltaic field through international peer review.
You will join the ACDC (Artificial intelligence, Characterisation, Defects, and Contacts) Research Group at SPREE. We are a diverse, world leading team under the supervision of Professor Ziv Hameiri. The team has won more than 9 best student and best poster awards at international conferences in the past 5 years. You can find more about the group on our website:
NOTE: Do not use the enquiry system below, check the application process here and send a direct email with all of the listed items.
If this project excites you, you can begin the application process by emailing the team directly at [Email Address Removed]. Please include the following:
- Up-to-date curriculum vitae (CV)
- Full undergraduate and masters academic transcript
- Copy of master’s thesis as well as links to publications (if applicable)
- List of 4-6 referees with official institutional emails
- A short video (< 7 minutes) discussing previous research experience