Taste of Research Summer Scholarships

2027 Projects - School of Photovoltaic and Renewable Energy Engineering

Photovoltaic and Renewable Energy Engineering Research Areas

Photovoltaic and Renewable Energy Engineering Projects

No School Research Area

Project Title:	A tale of dual-land and dual-spectrum utilisation: Down-conversion materials-based Agrivoltaic
Name of Supervisor:	Dr. Fandi Chen
Email of Supervisor:	fandi.chen@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Ziv Hameiri, Dr. Gaia Maria Javier
Email of Joint/Co-Supervisor:	ziv.hameiri@ unsw.edu.au; g.javier@unsw.edu.au
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Advanced Materials
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Agrivoltaics (AgriPV) combines photovoltaic electricity generation with agricultural production on the same land and is now being trialled internationally, including in Germany, France, Italy, Japan and Chile. In Australia, rapid growth in large?scale solar has intensified competition for productive farmland, and AgriPV offers a pathway to improve land?use efficiency while reducing crop heat stress, water loss and exposure to extreme weather. Conventional systems position opaque crystalline?silicon modules above or beside crops, enabling simultaneous food and energy production, but limiting the amount of photosynthetically active radiation reaching plants. This challenge has motivated spectrally selective AgriPV, which allocates different portions of the solar spectrum to crop growth and PV conversion. Down?conversion materials provide a promising strategy by absorbing less agriculturally useful wavelengths and re?emitting them at longer wavelengths that better match crop photosynthesis or PV device response. This project builds on that concept. Students will develop and experimentally evaluate a novel AgriPV module incorporating down?conversion materials to improve dual?spectrum utilisation. They will prepare luminescent materials and thin films, fabricate prototype modules, and characterise optical and PV performance using research?grade tools. The project also offers scope to investigate how material properties, spectral behaviour and device design influence both solar?cell performance and plant growth, contributing to a real?world sustainability challenge in Australia’s renewable?energy and agricultural sectors.
Research Environment:	You will work with the ACDC Research Group, a lively team of more than 20 researchers dedicated to solar energy research. The group has a friendly and supportive environment. You'll benefit from close mentoring, regular meetings with supervisors, and opportunities to share your work with other team members. In addition to your research, you'll also be able to take part in a variety of social activities with the team.
Novelty and Contribution:	.
Expected Outcomes:	The student will gain access to the chemical synthesis laboratory and optical characterisation equipment at SPREE. By the end of the project, the student is expected to develop fundamental skills in chemical synthesis, material preparation, and module fabrication. Dr Fandi Chen will also demonstrate a range of material characterisation techniques to help the student develop a broader understanding of materials science research and its application to the project.
Reference Material Links:	ACDC group website: https://www.acdc-pv-unsw.com/ Wavelength-selective agriPV: https://doi.org/10.1016/j.joule.2024.08.006 Down-conversion materials: https://doi.org/10.1016/j.joule.2026.102578
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Advanced characterisation of solar cells using luminescence imaging
Name of Supervisor:	Yan Zhu,
Email of Supervisor:	yan.zhu@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	The global market for photovoltaics (PV) has seen tremendous growth during the last few decades. With continuously increasing energy conversion efficiency and reduced cost, the electricity generated from PV is now cheaper than coal-fired power plants in Australia and many other places in the world. Characterisation plays a vital role in the optimisation of solar cells and making them more efficient. In particular, luminescence imaging is a powerful tool for spatially resolved inspection of solar cell quality, helping researchers and manufacturers quickly identify problems in solar cells. Photoluminescence (PL) imaging is a technique invented and commercialised by UNSW researchers. It has been widely used in all the value chain of PV. In our research group, we continue to improve luminescence imaging techniques to make them more powerful and applicable to more types of solar cells. In this project, we will develop novel characterisation techniques based on luminescence imaging for next-generation solar cells. You will be able to develop inspection techniques that can help improve the efficiency of state-of-the-art silicon solar cells, as well as multijunction solar cells.
Research Environment:	In this project, you will work with the ACDC research team (www.acdc-pv-unsw.com). The team focuses on the characterisation of solar cells. It is made up of around 20 researchers and students. The group has a very friendly environment. We have students who finished a ToR project with us, stayed for their 4th-year thesis, and later transitioned to a PhD in the group. You will mainly work with Dr. Yan Zhu. He has developed several novel characterisation techniques which have been well recognised in international PV research conferences. Many other experts in PV, including the inventor of PL imaging, will also help and guide you during this project. We will ensure a smooth start to your project, and throughout its duration, you will have a better chance to develop independent research skills.
Novelty and Contribution:	.
Expected Outcomes:	The outcome of this project will be novel characterisation techniques based on the adaption of conventional luminescence imaging. The method will be applied to diagnose the non-uniformity of some state-of-the-art silicon solar cells and next generation multijunction solar cells. Essential parameters of solar cells will be mapped using the developed techniques. Potentially, we will develop a prototype of a new characterisation tool. There is a chance to publish the outcomes as a conference paper or journal paper. Our previous ToR students presented their work in the US and Japan! Nevertheless, the most important outcome of the project will be you, an undergraduate student who enjoys doing research, and understanding the basic skills needed for conducting good research.
Reference Material Links:	The novel characterisation techniques to be developed in this project will benefit from the extensive research experience in novel luminescence imaging techniques of our group. We have several unique homemade luminescence imaging setups in our lab, all customised to meet different purposes. We will either modify one of our existing setups or build a completely new one. State-of-the-art solar cells to be investigated have also been provided by our industrial partners. Through the regular group meetings, all the other members of our research team will also use their expertise in characterisation to help you solve the problems you may encounter. We also have experts in numerical simulation, which is beneficial for our understanding of the measurement results. Website of our research group: https://www.acdc-pv-unsw.com/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Applying explainable AI to analyse defects in solar cell images
Name of Supervisor:	Gaia Maria Javier
Email of Supervisor:	g.javier@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Solar cell performance is often limited by defects such as cracks, which are visible in luminescence images. Identifying these defects and understanding their impact are critical for both manufacturing and long-term reliability. In this project, we will use explainable artificial intelligence (AI), a set of techniques that help us understand how AI models work, to study these images. In this context, explainable AI will be used to highlight the regions of each image most relevant to detecting and assessing defects. There are many models and explainable AI methods available, and students can choose which approach they want to explore. The project will be carried out in a collaborative research environment. Students will mainly work with one or two academic researchers and be part of a research team that includes undergraduate and postgraduate students. The aim of this project is to investigate how explainable AI can support a better understanding of solar cell performance through defect analysis. An interest in Python programming is encouraged. Even a basic familiarity will help you get the most out of the project, and we will support you in developing your skills further.
Research Environment:	You will work with the ACDC Research Group, a lively team of more than 20 researchers dedicated to solar energy research. The group has a friendly and supportive environment. You’ll benefit from close mentoring, regular meetings with supervisors, and opportunities to share your work with other team members. In addition to your research, you’ll also be able to take part in a variety of social activities with the team.
Novelty and Contribution:	.
Expected Outcomes:	Students will gain experience with image-based machine learning models and explainable AI. They will apply these methods to solar cell image data and present insights into how transparent models can support quality assessment.
Reference Material Links:	Relevant work can be found on the website of the ACDC Research Group: https://www.acdc-pv-unsw.com/publications.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Data-Driven Health Monitoring of Utility-Scale Solar Trackers
Name of Supervisor:	Deniz Ekin Canbay
Email of Supervisor:	d.canbay@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Utility-scale solar farms utilise large single-axis trackers to follow the sun and maximise energy capture. However, these mechanical systems are subjected to extreme weather and constant wear. When components degrade, the resulting downtime causes significant financial and energy losses. Currently, the industry struggles to identify these faults until catastrophic failure occurs. This project offers a hands-on introduction to predictive maintenance. Instead of relying on physical inspections, you will use real-world SCADA telemetry data (such as motor angles, current draw, and wind speed) to identify failing trackers remotely. You will build a simple dynamic model of how a healthy tracker should behave, and write a script to flag when the real-world data deviates from that baseline. This is an excellent opportunity to work with proprietary, utility-scale data and develop highly transferable skills in time-series analysis and fault detection. We will provide the foundational physics and guide you through the data processing pipeline.
Research Environment:	The project will be conducted within the ACDC research group at UNSW, providing access to a collaborative research environment and support from peers for discussion, problem-solving, and knowledge exchange.? You can use Python (Pandas, NumPy, SciPy, pvlib) or MATLAB. We will supply a small proprietary utility-scale SCADA telemetry dataset for you to work on.
Novelty and Contribution:	.
Expected Outcomes:	A simple mathematical model of a single-axis solar tracker. An algorithm capable of identifying mechanical degradation from raw telemetry data.
Reference Material Links:	Overview of single-axis solar tracker mechanics (https://www.mdpi.com/2076-3417/12/19/9682)
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Decision thresholds for second-life solar PV modules
Name of Supervisor:	Rama Sharma
Email of Supervisor:	rama.sharma@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	This project will investigate sustainable management pathways for decommissioned solar PV modules, with a focus on understanding when reuse, refurbishment or recycling may be the most appropriate option. Using data-driven economic and environmental analysis, the project will explore key factors that influence end-of-life decision-making and identify practical opportunities to support more circular and sustainable PV systems in Australia.
Research Environment:	The student will join a supportive, friendly and collaborative multidisciplinary research group, with opportunities to engage with researchers working across academic and industry-focused projects. The group encourages regular knowledge sharing, teamwork and informal social activities, creating a welcoming environment for learning, collaboration and professional development.
Novelty and Contribution:	.
Expected Outcomes:	The project will generate preliminary insights into sustainable pathways for decommissioned PV modules, identify key decision factors, and develop a simple analytical framework. The student will summarise the findings in a short presentation and a final research poster.
Reference Material Links:	"1. https://www.dcceew.gov.au/environment/protection/waste/solar-panels/ 2. https://www.acap.org.au/post/solar-panel-recycling-end-of-life-management-scoping-study/ 3. https://www.acdc-pv-unsw.com/"
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Detecting Solar PV Soiling from Power and Weather Data
Name of Supervisor:	Dr Abhnil Prasad
Email of Supervisor:	abhnil.prasad@unsw.edu.au
Name of Joint/Co-Supervisor:	A/Prof. Merlinde Kay and Prof. Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	"Dust and dirt accumulating on photovoltaic (PV) modules can reduce the amount of electricity they generate. This problem is particularly important in dry environments, where long periods without rainfall can allow significant soiling to develop. This project will investigate whether soiling can be detected using routinely measured solar power data rather than dedicated soiling sensors. The student will use publicly available measurements from the DKA Solar Centre in Alice Springs, including PV power generation, solar irradiance and rainfall. The student will develop a simple weather-normalised PV performance indicator and investigate how it changes during extended dry periods and following rainfall or known cleaning events. The project will provide hands-on experience working with real renewable-energy data and introduce the student to the process of developing and testing a research hypothesis."
Research Environment:	The student will work within the School of Photovoltaic and Renewable Energy Engineering at UNSW and will receive regular guidance on PV performance analysis, environmental data and scientific programming. The project is primarily computational and will use publicly available measurements from the DKA Solar Centre in Alice Springs. The student will work with Python-based tools for data processing, visualisation and analysis and will gain experience with the way real research questions are developed and tested using observational data. The scope is designed for an undergraduate student with basic programming experience; prior knowledge of PV soiling is not required.
Novelty and Contribution:	.
Expected Outcomes:	"1. Develop a Python workflow to download, quality-control and analyse PV power, irradiance and rainfall data. 2. Develop a simple normalised PV performance indicator that can be tracked through time. 3. Investigate whether PV performance gradually decreases during extended dry periods. 4. Examine whether rainfall or documented cleaning events result in measurable recovery in PV performance. 5. Estimate simple soiling rates for selected periods and discuss the uncertainty and limitations of the approach."
Reference Material Links:	DKA Solar Centre – Alice Springs data and PV systems (https://dkasolarcentre.com.au/locations/alice-springs)
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Development of SLAM-Based Autonomous Inspection System for Solar Power Plants
Name of Supervisor:	Jim Joseph John
Email of Supervisor:	j.joseph_john@unsw.edu.au
Name of Joint/Co-Supervisor:	Moonyong Kim, Bram Hoex
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	This project aims to develop a Simultaneous Localization and Mapping (SLAM) system for autonomous inspection of solar power plants. The system will enable mobile robots or drones to navigate, map, and inspect large-scale solar farms in real time without reliance on GPS, which is often unreliable in certain terrains or under panel shading.
Research Environment:	The research will be conducted in a multi-disciplinary setting combining expertise in AI and solarThis project aims to develop a Simultaneous Localization and Mapping (SLAM) system for autonomous inspection of solar power plants. The system will enable mobile robots or drones to navigate, map, and inspect large-scale solar farms in real time without reliance on GPS, which is often unreliable in certain terrains or under panel shading. energy systems.
Novelty and Contribution:	.
Expected Outcomes:	A SLAM-based navigation system for autonomous inspection robots or drones in solar farms. High-resolution 3D maps of solar plants with precise localisation.
Reference Material Links:	https://autowarefoundation.github.io/autoware-documentation/main/how-to-guides/integrating-autoware/creating-maps/open-source-slam/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Dynamic life cycle assessment (dLCA) of a Utility?Scale Solar PV
Name of Supervisor:	Dr. Rama Sharma
Email of Supervisor:	rama.sharma@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Ziv Hameiri
Email of Joint/Co-Supervisor:	z.hameiri@unsw.edu.au
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:
Abstract:	This project gives a student the opportunity to work closely with a senior researcher and be part of a diverse and supportive research group that includes postgraduate and PhD students working on photovoltaic (PV) characterisation, imaging techniques, artificial intelligence-based analysis for PV module quality assessment related topics. The student will receive regular guidance and feedback while learning how research is carried out in an academic environment. The aim of the project is to understand and estimate the environmental impacts of a large solar power plant in Australia over its full lifetime. Instead of assuming that conditions stay the same, the project looks at how things change over time, such as improvements in the electricity grid, the gradual ageing of solar panels and future advancement in the PV manufacturing. Using computational modelling and publicly available Australian data, the student will compare a traditional “one?off” static assessment with a time?based dynamical approach and explore what this means for the climate benefits of large?scale solar energy.
Research Environment:	The student will work in a supportive research group, closely supervised by an experienced researcher, with regular interaction with postdocs and postgraduate/PhD students. The environment provides opportunities for discussion, feedback, and learning how academic research is conducted in practice.
Novelty and Contribution:	.
Expected Outcomes:	1. Develop a python-based/computational model to simulate dLCA and compare results with the static LCA 2. Project report summary (aims, method, results and key findings), Poster presentation 3. Improved research skills, including working with data and interpreting results
Reference Material Links:	1. ISO 14040 (2006): International standard defining the principles and framework for Life Cycle Assessment. 2. IPCC AR6 (Working Group III)): Authoritative source on lifecycle greenhouse?gas emissions of energy technologies, including solar PV. 3. Jordan & Kurtz (2016): Key reference on long?term photovoltaic degradation rates used in PV performance modelling. 4. AEMO (2024) Integrated System Plan: Australia?specific projections of electricity system evolution and grid decarbonisation. 5. Australia?specific life?cycle inventory data. Australian Life Cycle Assessment Society (ALCAS). (2023). AusLCI: Australian Life Cycle Inventory Database. https://www.auslci.com.au
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Edge-AI Virtual Sensors: Turning Solar Panels into Weather Stations
Name of Supervisor:	Deniz Ekin Canbay
Email of Supervisor:	d.canbay@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	When a solar farm's power output drops, operators must quickly answer one question: is the equipment broken, or is a cloud passing over? The only way to know is by measuring the incoming sunlight. If a weather sensor shows bright sun but a panel produces low power, the equipment is failing. If the sensor shows low light and the power drops proportionally, it is just a cloud. Therefore, accurate sunlight data is the absolute baseline for monitoring a farm's health. However, physical weather sensors are expensive, spread too sparsely across large arrays, and frequently fail due to dust or bird droppings. When they fail, operators lose their baseline and cannot diagnose underperformance. This project solves that hardware problem with smart software. You will build a "Virtual Irradiance Sensor" powered by lightweight machine learning. Instead of measuring sunlight directly with a fragile physical instrument, you will train an AI model to reverse-engineer the amount of sunlight hitting a panel based strictly on its electrical output (current, voltage, and temperature). Once your model is trained on historical data, you will deploy it onto an embedded edge computer (e.g., a Raspberry Pi). This effectively turns the solar panels themselves into a dense, self-calibrating weather network, allowing the system to monitor itself without relying on physical sensors. This project is designed to bridge the gap between theoretical coursework and real-world engineering, equipping you with highly employable skills in AI and hardware deployment that are heavily sought after in the tech and renewable energy sectors.
Research Environment:	The project will be conducted within the ACDC research group at UNSW, providing access to a collaborative research environment and support from peers for discussion, problem-solving, and knowledge exchange.? Software/Tools: Python (Pandas, Scikit-learn, and/or PyTorch). Hardware: An edge computing device (e.g., Raspberry Pi or NVIDIA Jetson). Dataset: A small Proprietary utility-scale electrical and meteorological dataset provided by the UNSW ACDC research group.
Novelty and Contribution:	.
Expected Outcomes:	Train a baseline predictive model (using PyTorch or Scikit-learn). A benchmark report evaluating the hardware's prediction speed and accuracy.
Reference Material Links:	PV Fundamentals: PVeducation.org – The industry-standard interactive guide to solar physics and how environmental factors impact power generation. Machine Learning: Scikit-learn Getting Started – A highly practical, introductory guide to building baseline regression models in Python. https://scikit-learn.org/stable/getting_started.html Edge Deployment: PyTorch Edge Tutorials – Official documentation on how to compress and deploy machine learning models onto embedded hardware systems. https://pytorch.org/edge
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Enhancing silicon solar cell efficiency with down-conversion encapsulants
Name of Supervisor:	Victor Yu Zhang
Email of Supervisor:	yu.zhang34@unsw.edu.au
Name of Joint/Co-Supervisor:	Jessica Jiang, Ned Ekins-Daukes, Martin Green
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Solar panels made from silicon are already highly efficient, but ultraviolet (UV) light is not effectively converted into electricity by silicon solar cells. The valuable energy carried by UV is largely lost. This project explores an exciting solution called down-conversion — a process where one high-energy UV photon is converted into two lower-energy photons. These new photons can be used much more efficiently by silicon solar cells. The aim of this project is to boost cell efficiency while reducing UV-induced degradation, helping solar panels generate more power and last longer. You will contribute to developing and testing these novel light-converting materials and see how they perform on real, industry-grade silicon solar cells.
Research Environment:	working in an industry-linked research project, experiencing full pathway from lab to real-world solar devices
Novelty and Contribution:	.
Expected Outcomes:	chemical synthesis and testing of DC materials, experimental demonstration of efficiency enhancement via DC encapsulants
Reference Material Links:	https://doi.org/10.1063/1.1492021, https://doi.org/10.1021/acsenergylett.8b01528, https://doi.org/10.1021/acsnano.5c00487
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	From Pixels to Current: Fast, Contactless Short-Circuit Current Extraction with Machine Learning
Name of Supervisor:	Dr. Zubair Abdullah-Vetter
Email of Supervisor:	z.abdullahvetter@unsw.edu.au
Name of Joint/Co-Supervisor:	Dr. Arthur Julien, Prof. Ziv Hameiri
Email of Joint/Co-Supervisor:	a.julien@unsw.edu.au
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Photovoltaics (PV) is now the lowest?cost source of new electricity generation, yet continued improvements in manufacturing yield depend on fast, quantitative characterisation. As advanced cell architectures adopt multi?busbar, shingled/zero?BB, back?contact and tandem designs, conventional contacting becomes slow, unreliable and error?prone. The ACDC research group at SPREE has developed contactless photoluminescence?based workflows to extract key electrical parameters, but accurately determining short?circuit current (Jsc)—a direct indicator of cell efficiency—remains challenging. High?throughput production requires rapid measurements, yet faster acquisition reduces accuracy, especially for next?generation multijunction devices. This ToR project addresses this gap by developing fast, generalised machine?learning models that estimate Jsc from lower?resolution measurements. You will work within an experienced team specialising in PV characterisation, tool development and machine learning. The project involves applying optical and electrical modelling to silicon and emerging devices, exploring advanced characterisation equipment, and training/validating ML models to deliver rapid, accurate Jsc predictions across diverse cell architectures. The final outcome is an ML?assisted pipeline integrated with ACDC’s contactless Jsc tools, advancing both research and commercial capability in high?throughput PV characterisation. Supervision is provided by Dr Zubair Abdullah?Vetter, Dr Arthur Julien, and Prof. Ziv Hameiri, experts in ML?enabled PV characterisation, tandem/perovskite device physics, and contactless diagnostic technologies.
Research Environment:	You’ll join the ACDC group at SPREE, a collaborative PV lab advancing contactless characterisation and ML for PV applications. Day?to?day guidance from a postdoc (Zubair/Arthur), with senior oversight from Prof. Ziv Hameiri and access to the wider ACDC team. Access to our contactless Jsc equipment, high-power computing hardware, and foundational Python repositories and codes. Onboarding to data and safety procedures, weekly check?ins, and clear milestones aligned to ToR’s project?plan expectations.
Novelty and Contribution:	.
Expected Outcomes:	Develop a deeper understanding of contactless Jsc measurement approaches Design and develop a modelling workflow to synthesise next-generation solar cell data for ML training Design and develop a clean, reproducible ML pipeline to accurately extract Jsc from lower-resolution input data
Reference Material Links:	https://ieeexplore.ieee.org/document/11132431 https://ieeexplore.ieee.org/document/10749343 https://www.acdc-pv-unsw.com/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Integrating generative AI with solar cell images
Name of Supervisor:	Gaia Maria Javier
Email of Supervisor:	g.javier@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Advanced Materials
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Luminescence images provide critical insights into the performance and reliability of solar cells. In this project, we will apply generative artificial intelligence (AI) to these images. Generative models, such as those behind tools like DALL-E or Stable Diffusion, learn patterns from existing data and then create new examples. For solar cells, this could mean producing synthetic images to expand datasets, helping AI models learn patterns that represent image features more clearly, or experimenting with advanced methods for image analysis. Students can choose which of these directions they would like to explore. The project will be carried out in a collaborative research environment. Students will mainly work with one or two academic researchers and be part of a research team that includes undergraduate and postgraduate students. The aim of this project is to explore how generative AI can open new ways of analysing solar cell quality and performance. An interest in Python programming is encouraged. Even a basic familiarity will help you get the most out of the project, and we will support you in developing your skills further.
Research Environment:	You will work with the ACDC Research Group, a lively team of more than 20 researchers dedicated to solar energy research. The group has a friendly and supportive environment. You’ll benefit from close mentoring, regular meetings with supervisors, and opportunities to share your work with other team members. In addition to your research, you’ll also be able to take part in a variety of social activities with the team.
Novelty and Contribution:	.
Expected Outcomes:	Students will gain skills in image processing and generative AI. They will evaluate the performance of different generative methods and demonstrate their application to solar cell image data.
Reference Material Links:	Relevant work can be found on the website of the ACDC Research Group: https://www.acdc-pv-unsw.com/publications.
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Investigating Solar Energy Technologies for Space Applications
Name of Supervisor:	Anh Huy Tuan Anh
Email of Supervisor:	huytuananh.le@unsw.edu.au
Name of Joint/Co-Supervisor:	Ziv Hameiri
Email of Joint/Co-Supervisor:	z.hameiri@unsw.edu.au
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Space missions increasingly rely on compact and affordable satellites for communication, Earth observation, navigation, and scientific research. As satellite designs become smaller and more cost-sensitive, Si solar cells have regained attention due to their affordability, leading to a growing market share. However, solar cells in space must operate under conditions very different from those on Earth, including extreme temperatures, intense radiation, and the space environment. This project explores the performance and potential of advanced solar-cell technologies for future satellite and space applications. Understanding how these devices behave under space-relevant conditions is important for improving satellite lifetime, reducing system weight, and developing more affordable space missions. Students may investigate how solar cells respond to different temperatures, illumination conditions, and environmental stresses. The project may also involve comparing different cell designs, performing laboratory measurements, analysing experimental data, and using models to understand device behaviour. This project is suitable for undergraduate students interested in renewable energy, physics, materials science, electrical engineering, or space technology. It provides an opportunity to gain practical experience in solar-cell characterisation while contributing to the development of lightweight, reliable, and cost-effective power technologies for the growing space industry.
Research Environment:	The project will be conducted in a laboratory-based research environment focused on solar-cell characterisation and renewable-energy technologies. Students will work with advanced measurement equipment, analyse experimental data, and interact with researchers working in photovoltaics and space-related energy applications. Depending on the project scope, activities may include laboratory experiments, computer-based analysis, modelling, and discussions within a research team.
Novelty and Contribution:	.
Expected Outcomes:	Students will gain an understanding of solar-cell technologies and their potential applications in space. They will develop practical experience in experimental measurements, data analysis, and scientific interpretation. Depending on the project scope, students may also produce a technical report, analyse solar-cell performance under different conditions, and contribute to a broader research project.
Reference Material Links:	Suggested background reading: Silicon's cosmic comeback: Temperature-dependent performance and radiation stability of ultra-thin silicon heterojunction solar cells for space applications Link: https://www.sciencedirect.com/science/article/pii/S2468606926000584
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Multi-Agent AI System for Advanced Solar Power Plant Modeling and Optimization
Name of Supervisor:	Jim Joseph John
Email of Supervisor:	j.joseph_john@unsw.edu.au
Name of Joint/Co-Supervisor:	Moonyong Kim, Bram Hoex
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Intelligent & Autonomous Systems
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	This project aims to develop a multi-agent AI system to enhance the modeling, performance analysis, and optimization of solar power plants. The system will integrate multiple AI agents, each specialising in different tasks. These agents will collaborate and communicate through a shared framework, improving decision-making efficiency, reducing operational risks, and optimising power generation.
Research Environment:	The project will be conducted in an interdisciplinary research environment, involving collaboration between experts in AI and solar system modelling.
Novelty and Contribution:	.
Expected Outcomes:	A multi-agent AI framework capable of real-time solar plant simulation, monitoring, and optimisation. A scalable and customisable AI solution that can be applied across different solar power plant configurations.
Reference Material Links:	https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ https://towardsdatascience.com/multi-ai-agent-systems-101-bac58e3bcc47/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	See-Through Antimony Chalcogenide Solar Technology for BIPV
Name of Supervisor:	Dr. Chen Qian
Email of Supervisor:	c.qian@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Advanced Materials
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Solar energy is usually captured by dark, opaque panels placed on rooftops. But what if windows could also generate electricity while still letting light through? This project explores the development of a new type of semi-transparent solar cell based on an material called antimony chalcogenide. Unlike traditional silicon solar panels, this material can allow visible light to partially pass through while generating electricity, making it suitable for use in building-integrated applications such as power-generating windows. The aim of this project is to investigate how we can improve the transparency and energy efficiency of these solar cells at the same time. The student will help study how light interacts with the material, how device design affects performance, and how these solar cells could contribute to more sustainable buildings in the future. The student will work closely with a postdoc researcher and join a supportive research team. Through this experience, the student will gain hands-on exposure to laboratory research, learn basic fabrication and testing techniques, lab-to-field transition and participate in team discussions about renewable energy technologies. This project offers an opportunity to explore how innovative materials research can contribute to greener cities and the transition to clean energy.
Research Environment:	The student will work closely with a postdoc researcher and join a supportive research team. SPREE will provide world-leading lab facilities to support this program.
Novelty and Contribution:	.
Expected Outcomes:	By the end of the project, we expect to generate a clear dataset that shows the relationship between light transparency and solar cell performance in a 5cmx5cm device. This dataset will help us understand how increasing transparency affects energy output and will guide future optimisation of semi-transparent solar cells.
Reference Material Links:	https://doi.org/10.1002/adma.202303936
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Project Title:	Split the sun: Optimising agrivoltaic systems with modelling and machine learning
Name of Supervisor:	Dr. Gaia Maria Javier
Email of Supervisor:	g.javier@unsw.edu.au
Name of Joint/Co-Supervisor:	Prof. Ziv Hameiri, Dr. Fandi Chen
Email of Joint/Co-Supervisor:	ziv.hameiri@unsw.edu.au; fandi.chen@unsw.edu.au
School:	School of Photovoltaic and Renewable Energy Engineering
Faculty Research Area (Theme):	Energy Systems, Renewable and Non-Renewable
Applicable to other Engineering schools/disciplines:
Terms:	Summer
Abstract:	Agrivoltaics (AgriPV) is an emerging approach that integrates solar energy generation with agricultural production, using the same land for both electricity generation and crop production to improve land-use efficiency and support sustainable food-energy systems. Designing an effective agriPV system, however, requires balancing competing objectives, including light availability, shading, spectral quality, and heat and water stress, across both electricity generation and crop growth. Because these trade-offs are complex and interdependent, achieving an effective balance requires a systematic approach. This project aims to use physical modelling and machine learning to optimise agriPV system design and improve the combined performance of electricity generation and crop growth, without relying solely on field trials. Students will apply physical simulation models of agriPV systems, for example modelling how panel geometry and spacing affect light and heat distribution across a crop canopy, and use machine learning to optimise design based on these models. This may include efficiently searching design options such as panel angle and spacing, or building models that predict PV output or crop yield faster than a full simulation. The specific research direction can be tailored to student interest, and the project will be carried out in a collaborative research environment, with students working mainly alongside two or three academic researchers as part of a wider team that includes both undergraduate and postgraduate students. An interest in Python programming is encouraged. Even a basic familiarity will help you get the most out of the project, and we will support you in developing your skills further.
Research Environment:	You will work with the ACDC Research Group, a lively team of more than 20 researchers dedicated to solar energy research. The group has a friendly and supportive environment. You'll benefit from close mentoring, regular meetings with supervisors, and opportunities to share your work with other team members. In addition to your research, you'll also be able to take part in a variety of social activities with the team.
Novelty and Contribution:	.
Expected Outcomes:	Students will gain practical experience in physical modelling and simulation of energy systems, along with hands-on experience applying machine learning techniques for design optimisation. They will develop Python programming skills applied within real research context and build an understanding of the trade-offs involved in designing sustainable, multi-purpose energy and agricultural systems.
Reference Material Links:	ACDC group website: https://www.acdc-pv-unsw.com/
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No

Projects offered by other Engineering Schools that may be of interest are:

Project Title:	Combination Therapy with Antimicrobial Peptides to Combat Multidrug-Resistant Bacteria
Name of Supervisor:	Edgar Wong
Email of Supervisor:	edgar.wong@unsw.edu.au
Name of Joint/Co-Supervisor:	.
Email of Joint/Co-Supervisor:	.
School:	School of Chemical Engineering
Faculty Research Area (Theme):	Health & Medical Technologies
Applicable to other Engineering schools/disciplines:	Biomedical Engineering Computer Science & Engineering Mechanical & Manufacturing Engineering Photovoltaic and Renewable Energy Engineering
Terms:	Summer
Abstract:	Antimicrobial resistance (AMR) is now considered a critical global healthcare challenge and urgently requires new therapeutic strategies to overcome this issue. Antimicrobial peptides (AMPs) and mimics thereof have been shown to effectively synergise and revive the 'lost' activity of antibiotics against multidrug-resistant bacteria. This approach is promising in combating AMR and we aim to build upon our initial work and develop further. The project will look at testing more combinations and against wider panel of bacteria including priority pathogens such as Klebsiella pneumoniae and Acinetobacter baumannii.
Research Environment:	Very biofocussed project and hence the scholar will be mainly working in a PC2 microbiology lab to perform antimicrobial assays. Scholar needs to have good attention to detail.
Novelty and Contribution:	.
Expected Outcomes:	Tested various combinations of AMPs and antibiotics against different bacteria strains. The results are expected to lead to high impact publication and also further in vivo testing in animal models with collaborators, which would form the basis of preclinical work for future translation. The scholar will learn/enhance technical skills at working in a biolab and also develop deep knowledge in the AMR field.
Reference Material Links:	https://www.edgarwonglab.com/ https://pubs.acs.org/doi/full/10.1021/acsinfecdis.2c00087 https://pubs.acs.org/doi/full/10.1021/acs.biomac.4c01137 General reading on antimicrobial peptides (AMPs) and combination therapy
Will the student visit the premises of an industry partner, or undertake any activity on premises external to UNSW?	No