Integrating Machine Learning and Advanced Characterisation for Next-Generation Photovoltaics

Introduction

The Energy Materials Hybrid Lab (EMHL) at Eindhoven University of Technology (TU/e) is seeking a highly motivated PhD candidate to join our research at the interface of experimental physics, materials science, and artificial intelligence. EMHL is a newly established research group within the Materials to Devices (M2D) research group, one of Europe’s leading research environments for next-generation optoelectronic materials and photovoltaic technologies. As part of EMHL and M2D, you will join a vibrant interdisciplinary community and have access to state-of-the-art facilities for thin-film fabrication, advanced optoelectronic characterisation, spectroscopy, microscopy, device processing, and photovoltaic testing.

This PhD project focuses on perovskite and perovskite-based tandem solar cells, aiming to combine advanced experimental characterisation with machine learning to better understand, predict, and improve photovoltaic device performance and stability. The project is fundamentally an Experimental Physics PhD, with approximately 50% of the work dedicated to laboratory research and 50% to scientific programming, data science, and machine learning development, offering a unique opportunity to develop expertise across both experimental and computational research.

Job description

Metal halide perovskite solar cells are among the most promising technologies for the next generation of photovoltaics, with the potential to deliver highly efficient and low-cost solar energy. Despite their remarkable progress, many of the fundamental processes that determine their performance and long-term stability remain poorly understood, limiting their commercial deployment. At the same time, modern characterisation techniques generate an unprecedented amount of experimental data. Each measurement captures a different aspect of the material, but no single technique can fully reveal the complex physical processes occurring inside a photovoltaic device. This project explores a new approach to materials research by combining multiple complementary characterisation techniques with physics-based modelling and artificial intelligence to create a more complete picture of how these energy materials function.

In this PhD project, you will learn how to integrate machine learning into photovoltaic research. You will work on AI-enhanced multimodal characterisation methods that combine advanced experiments, physics-based modelling, and machine learning to uncover the mechanisms governing device performance and degradation. Rather than using AI simply as a prediction tool, you will help develop new machine learning methods that allow scientists to interpret complex experimental data, identify hidden patterns, and gain new physical insights. You will fabricate and characterise state-of-the-art perovskite and perovskite-silicon tandem solar cells while gaining expertise in scientific programming and modern machine learning techniques to transform experimental data into a deeper understanding of photovoltaic materials.

Your research may include

• Fabrication and characterisation of perovskite and perovskite-silicon tandem solar cells
• Advanced optoelectronic measurements, including photoluminescence, electroluminescence, imaging, and electrical characterisation
• Learning to develop, deploy and interpret machine learning models that combine input from multiple characterisation techniques
• Learning to apply best practices for data science, data storage and databases
• Integrating experimental data with physics-based simulations

Working within the Energy Materials Hybrid Laboratory (EMHL) and the broader Materials to Devices (M2D) research group, you will collaborate with researchers in experimental physics, materials science, chemistry, and artificial intelligence, while benefiting from access to world-class facilities for thin-film fabrication, advanced optoelectronic characterisation, and photovoltaic device development.

Job requirements

You have

• A Master’s degree (or equivalent) in Applied Physics, Physics, Materials Science, Nanotechnology, Electrical Engineering, Chemistry, Chemical Engineering, or a closely related discipline
• A strong interest in experimental research and advanced characterisation of semiconductors or energy materials
• An enthusiasm for learning scientific programming, machine learning, and data-driven approaches to materials research
• Experience with either experimental laboratory work or scientific programming/data analysis. Experience in both is an advantage, but not required
• Basic programming experience (e.g. Python, MATLAB, or similar) or a strong motivation to develop these skills
• Excellent analytical and problem-solving abilities
• Strong communication skills and excellent written and spoken English
• The ability to work both independently and collaboratively in an interdisciplinary and international research environment

Experience in one or more of the following areas is considered an advantage

• Perovskite or other semiconductor materials
• Photovoltaics or optoelectronics
• Optical or electrical characterisation techniques
• Machine learning, data science, or scientific computing
• Numerical modelling or device simulations

Most importantly, we are looking for someone who is excited to work across disciplines and eager to develop expertise in both experimental physics and AI-driven materials research. If you meet many, but not necessarily all, of the qualifications above, we encourage you to apply!

Application

We invite you to submit a complete application by using the apply button. The application should include:

• A cover letter in which you describe your motivation and qualifications for the position, including your knowledge and expertise in the fields mentioned above.
• A curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.

Ensure that you submit all the requested application documents. Please note that incomplete applications may not be considered and could be rejected.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.