This fully funded PhD opportunity at the University of Liverpool focuses on the automated virtual and physical screening of organic molecules for optoelectronic device applications, including displays, sensing, catalysis, and energy. The project leverages a combination of high-throughput computational chemistry and automated experimental characterization to identify and optimize molecular materials with desirable photophysical properties such as fluorescence lifetimes, singlet-fission rates, and up-conversion efficiency. While virtual screening of conjugated molecules is well established, predicting key photophysical properties remains a challenge due to limited and inhomogeneous experimental data. This research aims to expand virtual screening capabilities to hundreds of thousands of molecules and develop automated optical time-resolved characterization methods to generate reliable datasets, enabling fine-tuning of computational models.

Supervised by Professor Alessandro Troisi (Theoretical Chemistry & Spectroscopy) and Dr. John Ward (Organic Chemistry & Automation), the project benefits from expertise in high-throughput screening, prediction of photophysical properties, and automation in chemical characterization. The Materials Innovation Factory (MIF) provides state-of-the-art facilities for both computational and experimental work, and the project is part of the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry, which offers interdisciplinary training in robotics, digital methods, and materials science. The CDT program is designed in collaboration with 35 industrial partners to produce versatile researchers capable of cross-domain communication and innovation.

The studentship covers full home tuition fees and a maintenance grant for four years, with additional support for research consumables and conference attendance. Outstanding international candidates may be eligible for scholarships to cover the fee difference. The University of Liverpool is committed to diversity and inclusion, offering reasonable project adaptations and support for students with disabilities or caring responsibilities.

Applicants should have a strong academic background in Chemistry, Chemical Engineering, Materials Science, Physics, or related fields, and an interest in computational chemistry, spectroscopy, or automation. The application deadline is March 31, 2026, but early application is encouraged as interviews are conducted on a rolling basis. For informal enquiries, contact Professor Troisi at [email protected]. Please review the CDT guide for application instructions, and ensure you include the project title and reference number CCPR166 when applying online.

Relevant references and further details can be found in the project listing and associated publications.