Artificial Intelligence for Photovoltaics (fully funded)

About the Project

Open position available for PhD student!

If you are passionate about the application of AI in engineering science, we invite you to join us to shape the future of photovoltaics (PV) technologies together at the Australian National University (QS ranking 2026: No. 32 World-wide)! Contact: hualin.zhan@anu.edu.au

Group website: www.nexsas.org

Our first-year PhD students are already publishing in top-tier journals such as Advanced Materials, Advanced Functional Materials, etc. Only top students will be considered!

Eligibility and Selection Criteria

Applicant is expected to have a strong background in Computer Science, Computational Materials Science, or Perovskite Photovoltaics.

• Academic Excellence (see ANU admission requirements for further details):
1. International applicant should rank within the top 5% of their graduating class from a highly regarded university.
2. Australian and New Zealand applicant should hold at least an Upper Second-Class Honours (H2A) or equivalent qualification.

• Research Achievements (applicants with a GPA below 3.2 / 4.0 must demonstrate research excellence through one of the following):
1. At least one first-authored publication in a high-impact journal or conference (e.g., Impact Factor ≥ 20, NeurIPS, ICML, etc.), or
2. At least three first-authored publications in high-quality journals or conferences (e.g., Impact Factor ≥ 5), or
3. At least three patents.

Funding

The successful candidate will receive an annual tax-free stipend of AUD $39,069 together with a full tuition waiver.

Research Resources

The successful candidate will have access to state-of-the-art computational and experimental facilities, enabling cutting-edge research at the interface of AI, materials science, and photovoltaics.

• Computational Resources
  1. National Computational Infrastructure (NCI) with millions of service units available to our group.
  2. Group high-performance workstations featuring high-performance CPU and GPU, including Intel Core Ultra 9 series, AMD Threadripper Pro 7965WX, NVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX 4090, and more.
  3. Extensive licensed software, including VASP, Gaussian, COMSOL, and other advanced simulation tools.

• Experimental Resources
  1. World-class perovskite photovoltaic fabrication and characterisation laboratories, where world-record solar cells have been demonstrated.
  2. Australia’s most advanced high-throughput robotic platform for perovskite fabrication and characterisation—fully compatible with AI-guided experimental design.
  3. Access to national-level facilities, including the Australian National Fabrication Facility (ANFF), for device fabrication and advanced materials characterisation.

• Collaboration Networks
  1. Embedded within the Australian Centre for Advanced Photovoltaics (ACAP), with the ANU group as a key member.
  2. Broad international and domestic collaborations across China, Germany, the US, the UK, and leading Australian universities.

About the Project: Physics-based AI for Perovskite Photovoltaics

Join a pioneering research program at the ANU — a global leader in next-generation solar technology. Our group has achieved world-record perovskite solar cells, with publications in Nature, Science, and Energy & Environmental Science. These breakthroughs have unveiled both extraordinary opportunities (e.g., high performance) and critical challenges (e.g., low stability) that will define the future of solar energy.

This project aims to tackle these challenges by harnessing machine learning and physics-based AI to achieve precise control over perovskite stability and enable the rational design of efficient, stable perovskite-based solar cells. Building upon our in-house AI platform, AiNU, this PhD will contribute to transforming how materials discovery and device optimisation are conducted — from intuition-driven to AI-empowered science.

Successful candidate will work in a friendly environment with access to world-class photovoltaics fabrication, characterization, and computation facilities (as listed above). Candidate is expected to attend domestic and international conferences.

Candidate with a keen interest in technological revolution of perovskite photovoltaics using interpretable AI and machine learning are encouraged to apply. Experience in machine learning and computational materials science is highly preferred.