Vacancies

Open positions available for PhD students!

If you are passionate about the application of AI in engineering science, we invite you to join us to shape the future of sustainable energy technologies together at the Australian National University (QS ranking 2024: No. 30 World-wide)! Contact: [email protected]

Eligibility:

• Top 5% in class from highly regarded universities (international students) or ≥ upper 2nd class Honours (Australians or New Zealand citizens).
  • Click here to see more about the ANU requirements.
• Applicants should have published (if GPA<3.2/4, this is a must):
  • ≥1 first-authored high-impact journal/conference article (e.g., impact factor≥20, NeuralIPS, ICML, etc),
  • or ≥3 first-authored high-quality journal/conference article (e.g., impact factor≥5),
  • or ≥3 patents.

Photovoltaics: Physics-based Machine Learning for Perovskite

We are within a larger 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 - AiNU - that is ideal for future PhD studies on this project.

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 embodied AI and machine learning are encouraged to apply. Experience in machine learning and mathematics is highly preferred.

Hydrogen energy: AI-uncovered transport phenomenon at the tri-phase interface

This research investigates the transport of electrons, ions, and molecules at the tri-phase interface in hydrogen energy systems. We will investigate the influence of interfacial electric fields, electric double layers, ion-specific adsorption, and water structuring in nanoporous materials on hydrogen-electricity conversion efficiency.

Successful canditates will work closely with the Chinese Academy of Sciences, in which both state-of-the-art lab-scale and industry-scale hydrogen energy systems are available. Candidates will enroll at the ANU and split their time equally between the ANU and the Chinese Academy of Sciences (under the supervision of Prof Lianhai Zu), with each institution providing advanced training and research opportunities.

Candidates are expected to have a strong background in materials science, chemistry, physics, computer science, or related fields. Experience or a keen interest in artificial intelligence, theoretical electrochemistry or computational materials (e.g., DFT, MD), or clean energy technologies will be highly advantageous.