Machine learning for photovoltaics Australian National University in Australia

PhD Program: machine learning for photovoltaics Institution: Australian National University (QS ranking 2024: No. 30 World-wide) Research areas: Perovskites, Machine Learning for Science, Photovoltaics (perovskite single-junction, perovskite/silicon tandem, etc) Program start date: Any time after acceptance Scholarship deadlines:  End of August for International students (university scholarship). End of September for Australian students (university scholarship). Any time for project-funded and externally funded students Mode of study: Full-time  Supervisors: Prof Kylie Catchpole, FAA, FTSE Prof Klaus Weber Dr Heping Shen Dr Hualin Zhan ([email protected] contact for this program) Eligibility:  Majoring in Physics, Computer Science, Mathematics, and Materials Science. A Master’s degree or a Bachelor’s degree with First Class Honours. Top 5% in class from highly regarded universities (https://cecc.anu.edu.au/study/phd-mphil/pre-application-process/).  Program description:  We are a world-leading group in the field of photovoltaics. Our world-record perovskite solar cells have identified great opportunities and key challenges to revolutionize the next-generation photovoltaics (publications in Nature, Science, Energy Environ. Sci., etc.). This project aims to address these key challenges, in which we will use machine learning to deliver a step-change in the field’s capacity to precisely control the stability of perovskite and to rationally design the perovskite-based solar cells. We have already developed a prototype machine learning platform that is ideal for future PhD studies on this project (recently published in Energy Environ. Sci.). Successful candidates will work in a friendly environment with access to world-class photovoltaics fabrication, characterization, and computation facilities, such as the Australian Centre for Advanced Photovoltaics, Australian National Fabrication Facility, National Computational Infrastructure, etc. The ACT nodes of these facilities are all hosted by the Australian National University. Candidates will have opportunities to visit our close collaborators in Germany, United States, China, and other states in Australia. Candidates are expected to attend domestic and international conferences. Candidates with a keen interest in technological revolution of perovskite photovoltaics using machine learning are encouraged to apply. Experience in machine learning, physics, mathematics, and/or perovskite materials engineering is highly preferred. If you are passionate about engineering science and impactful research, we invite you to apply for this exciting opportunity to shape the future of sustainable energy technologies.  Selected publications:  H. Zhan, et. al., Energy Environ. Sci., 2024, DOI: 10.1039/D4EE00911H. J. Peng, et. al., Nature, 2022, 601, 573-578. J. Peng, et. al., Science, 2021, 371, 390. H. Shen, et. al., Energy Environ. Sci., 2018, 11, 394-406.