Looking for a research student - Machine learning application for luminescence imaging
Looking for a research student - PhD at the University of New South Wales, Sydney, Australia
Topic: Machine learning application for luminescence imaging
Contact: Dr Ziv Hameiri (email@example.com).
PhD at the University of New South Wales, Sydney, Australia
The School of Photovoltaic and Renewable Energy Engineering (SPREE) is one of the nine schools within the Faculty of Engineering at University of New South Wales (UNSW), Sydney, Australia. SPREE grew out of the Australian Research Council Photovoltaics Centre of Excellence in response to the growing industry of renewable energy. The school is widely considered as the best in the world. Building on its world-leading research, the school attracts leading international researchers in the area of photovoltaic.
Our academic staff has been consistently ranked amongst the leaders worldwide in the photovoltaic field through international peer review. Our team has held the world record for silicon solar cell efficiencies for over twenty years and has been responsible for developing the most successfully commercialised new photovoltaic technology internationally throughout the same period. The solar cell technology that is predicted to dominate the market in the next decade (the ‘PERC’) was invented and developed in our school.
We are looking for an excellent student for a novel project involving machine learning and advanced characterization (details below). The PhD project will be run in our state-of-the-art laboratories in close collaboration with the School of Computer Science Engineering (CSE). The School of Computer Science and Engineering is one of the largest and most prestigious computing schools in Australia. It offers undergraduate programs in Software Engineering, Computer Engineering, Computer Science and Bioinformatics, as well as a number of combined degrees with other disciplines. It attracts excellent students who have an outstanding record in international competitions. For further information about the School, please visit http://www.cse.unsw.edu.au/
Suitable students will be awarded a full scholarship for 3.5 years (PhD duration in Australia is 3-3.5 years). The scholarship fully covers the university fees and provides additional allowance to cover living costs:
Tuition fees: $45,000 per year
Living allowance: ~$27,000 per year
Conference allowance: $3,000 per conference (to support attending a scientific international conference; at least two conferences during the PhD).
Undergraduate Degree: Bachelor degree in a Computer Science or Computer Engineering with a graduation GPA above 8 out of 10 or equivalent.
Masters Degree: Priority will be given for those who graduated from a Masters by research program, focusing on machine learning, big data or similar.
Supervision will be done by Dr Ziv Hameiri, Prof Arcot Sowmya (CSE) and Prof. Thorsten Trupke (SPREE).
For more details please contact Dr Ziv Hameiri (firstname.lastname@example.org).
Machine learning application for luminescence imaging
The aim of this project is to develop machine learning algorithms for analysing luminescence-based images in order to improve the efficiency of solar cells.
Photoluminescence (PL) – the emission of light from a material after the absorption of photons – has been proven to be a very powerful monitoring tool for silicon-based solar cells. PL imaging is a measurement approach that was developed at UNSW and is commonly used for silicon wafers and silicon solar cells. Since the first proof of concept studies in the characterisation labs in SPREE, this technology has seen rapid adoption worldwide by researchers and companies and is now one of the most widely used techniques. For silicon devices, PL imaging is frequently used to monitor essential electrical parameters such as minority carrier lifetime, implied open circuit voltage, diode saturation currents, series resistance, shunt resistance and pseudo fill factor. The contactless nature of the measurement and the fact that it can be performed even on non-completed devices makes it an ideal tool to investigate various limiting processes within silicon wafers and silicon solar cells. UNSW has an internationally leading position in the growth of PL as an effective characterisation tool for silicon photovoltaics. This project will benefit from the large knowledge and experience in SPREE on various PL technologies in developing new groundbreaking PL-based applications for silicon and non-silicon solar cells.
The main project aims are to:
- Develop machine learning algorithms to extract various electrical properties for luminescence images of silicon wafers, solar cells and photovoltaics modules
- Develop machine learning algorithms to improve reliability of photovoltaics systems
- Develop machine learning algorithms to develop the new generation of solar cells
- To save the world!