PhD in Porous Molecular Framework Materials: This fully-funded PhD opportunity focuses on the application of machine learning to understand the effects of isomerization on adsorption and diffusion in porous Molecular Framework Materials (MFMs). The project, backed by the Leverhulme Trust, aims to develop a machine learning platform that models the local structural dynamics of MFMs, which are crucial for various applications due to their porous and tunable nature.
PhD in Machine Learning of Isomerization in Porous Molecular Framework Materials
Designation
PhD Researcher
Research Area
Machine Learning, Computational Chemistry, Porous Molecular Framework Materials (MFMs) including MOFs, COFs, ZIFs, and MOPs.
Location
School of Science and Technology
Eligibility/Qualification
- Minimum 2:1 Honours degree in Chemistry, Physics, Mathematics, or a related discipline.
- Experience in Python programming is advantageous.
Job Description
The successful candidate will collaborate with a Postdoctoral Research Assistant (PDRA) to calculate Potential Energy Surfaces for various adsorbate molecules within MFMs. The research will explore how local structural isomerism influences the overall properties and behaviors of these materials, addressing the currently existing gap in computational chemistry regarding structural disorder.
How to Apply
Interested candidates should submit their applications through the provided link. The application process includes detailed submission guidelines, including documentation of academic qualifications and any relevant experience.
Last Date for Application
Applications close on Tuesday, June 24, 2025.
For further inquiries, please contact Dr. Matthew A. Addicoat at matthew.addicoat@ntu.ac.uk.
Take advantage of this exciting opportunity to contribute to cutting-edge research in molecular materials and machine learning!
