PhD in AI for PV

Fully funded PhD position: Artificial Intelligence for Photovoltaics

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: Marco Ernst

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

Eligible and Selection Criteria

Applicant is expected to have a strong background in Computer Science or Silicon Photovoltaics.

Academic Excellence (see ANU admission requirements for further details):

International applicant should rank within the top 5% of their graduating class from a highly regarded university.
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):

At least one first-authored publication in a high-impact journal or conference (e.g., Impact Factor ≥ 20, NeurIPS, ICML, etc.), or
At least three first-authored publications in high-quality journals or conferences (e.g., Impact Factor ≥ 5), or
At least three patents.

Funding

The successful candidate will receive an annual tax-free stipend of AU$40,475 per annum (Full-time base rate 2027) together with a full tuition waiver.

The project is fully supported through the lead supervisor's active research grants.

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 and photovoltaics.

Computational Resources

High-performance workstations featuring high-performance CPU and GPU, including AMD Threadripper 7970X, AMD Threadripper Pro 7965WX, NVIDIA RTX PRO 5000 Blackwell, NVIDIA RTX 4090, and more.
Access to National Computational Infrastructure (NCI) and ARDC's Nectar Research Cloud.

Experimental Resources

World-class silicon photovoltaic fabrication and characterisation laboratories, where world-record solar cells have been demonstrated.
Access to national-level facilities, including the Australian National Fabrication Facility (ANFF), for device fabrication and advanced materials characterisation.

Collaboration Networks

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

About the Project: Physics-based AI for Photovoltaic Module Analytics

Join a leading photovoltaic research program at the Australian National University (ANU) developing advanced approaches for understanding and predicting the long-term performance of solar photovoltaic modules. As new photovoltaic technologies move rapidly from laboratory development towards large-scale deployment, improved methods are needed to identify emerging performance losses, understand degradation behaviour, and predict future module performance under real operating conditions.

This PhD project will develop physics-informed machine learning and data-driven methods for photovoltaic module analytics, combining high-resolution outdoor measurements with laboratory characterisation and accelerated testing data. The research will investigate how electrical performance, environmental exposure and operating conditions can be used to identify evolving module behaviour and develop probabilistic forecasts of long-term performance and degradation.

The project will sit at the intersection of photovoltaics, data science and physical modelling. Depending on the candidate's background and interests, the work may involve time-series analysis, machine learning, Bayesian or probabilistic modelling, photovoltaic device and module modelling, uncertainty quantification, and analysis of large experimental datasets. A key emphasis will be on developing models that remain physically interpretable rather than relying solely on black-box prediction.

The successful candidate will work within a collaborative research environment with access to advanced photovoltaic testing, outdoor monitoring, characterisation and computational facilities at ANU, together with datasets generated through a broader collaborative research program. The project will provide opportunities to work with researchers and industry partners across Australia and to present results at national and international conferences.

Candidates with a strong interest in photovoltaics, renewable-energy analytics, machine learning, statistical modelling or computational engineering are encouraged to apply. Experience in Python, data analysis, machine learning, photovoltaic modelling or related experimental research would be advantageous, but applicants are not expected to have expertise across all of these areas.

Fri Aug 14, 2026