PhD in AI-driven Chemical Synthesis Pathways and Machine Learning for Chemistry

Deakin University's Applied Artificial Intelligence Initiative is offering a PhD position focused on the intersection of artificial intelligence, chemistry, and materials science. The project aims to develop machine learning methods to efficiently enumerate and compute reaction networks on surfaces, with applications in chemical synthesis, battery materials, perovskites, new polymers, and more. The research will involve creating new physics-informed machine learning models to enhance the accuracy and efficiency of computational chemistry, particularly for surface reactions and catalysis. The successful candidate will work under the supervision of Dr Sherif Abbas and Prof Truyen Tran, both based at Deakin University in Australia. The project will address key challenges in predicting chemical reaction pathways, leveraging AI to model molecular dynamics and reaction networks. The research has broad applications, including corrosion science, catalysis, and the exploration of new synthesis pathways for advanced materials. Applicants should have a postgraduate degree in computer science, physics, chemistry, or mathematics, and must have published at least one article in a peer-reviewed journal related to AI fundamentals, AI applications, or chemistry. The position is ideal for candidates interested in interdisciplinary research at the crossroads of AI and the physical sciences. To apply, candidates should contact Dr Sherif Abbas or Prof Truyen Tran via email for further information and application instructions. The post does not specify funding details or a deadline, so early inquiries are encouraged. For more information, visit the provided LinkedIn and project links.