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Dr Brendan Wright


Fields of Research (FoR)

Condensed matter physics, Statistical data science, Machine learning, Photovoltaic devices (solar cells)


My Grants

  • Australian Renewable Energy Agency (ARENA) R&D Project Grant, "Machine learning applications for utility-scale...view more

My Grants

  • Australian Renewable Energy Agency (ARENA) R&D Project Grant, "Machine learning applications for utility-scale PV", RG220681, Dec 2022
  • Australian Centre for Advanced Photovoltaics (ACAP) Industrial Collaboration Grant, "End-of-Life Classification of Solar Panels", Mar 2022

My Qualifications

  • Ph.D. Physics (2018)
    Intelligent Polymer Research Institute, University of Wollongong
    Major: Physics of Excited-States in Condensed-Matter
    Supervision: Prof. Attila Mozer & Assoc. Prof. Tracey Clarke
    Thesis: ”The driving force dependence of charge carrier dynamics in donor-acceptor organic photovoltaic systems using optical and electronic techniques”
  • B. Nanotechnology Honours (2011)
    School of Chemistry and Molecular Bioscience, University of Wollongong
    Major: Computational Chemistry
    Supervision: Assoc. Prof. Haibo Yu
    Thesis: ”Development of a polarisable force field for Nafion”

My Awards

  • Winning Entry, 1st International Eurovision AI Song Contest, hosted by VPRO, Netherlands, May 2020; ”Beautiful the World” available at; Australian team as collaboration between academics at RMIT and UNSW, and industry partner Uncanny Valley; personal contribution included developing raw audio and song structure generation capabilities with learning algorithms.

My Research Activities

Current research at UNSW focuses on the study of electrochemical defect mechanisms in silicon photovoltaics; modelling state-space dynamics using generative representation learning to investigate underlying physical mechanisms; more broadly, utilising recent developments in machine learning to model complex dynamic systems.

My Research Supervision

Areas of supervision

  • Development of generative representation learning systems to model complex dynamics of physical systems from experimental characterisation data, focusing on excited-state physics within photovoltaic systems (charge-carrier generation, transport, recombination

Currently supervising

  • Chukwuka Madumelu; secondary supervision of doctoral candidate in photovoltaic engineering, University of New South Wales, 2019-2022
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