Postdoctoral Research Associate in Computational Chemistry

The Duarte Group works at the forefront of computational chemistry, developing predictive tools for reaction modelling and molecular design across catalysis, sustainability, and health. Alongside fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train.

 Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC-funded project, ML4MetaLigM.

We are seeking a highly motivated Postdoctoral Research Associate to contribute to the development and deployment of MLIPs for supramolecular modelling, with a focus on self-assembly, molecular binding, and catalysis.

 The project aims to overcome current data and methodological limitations to enable predictive modelling of complex supramolecular systems.

This post is fixed term for 2 years

You will lead and manage your own research within the project, developing and applying advanced electronic structure and molecular simulation methods. 

The work will involve transition-metal modelling, enhanced sampling techniques, and multiscale approaches, alongside contributing to the development and improvement of robust scientific software.

You will analyse data, refine hypotheses, publish in leading journals, present at international conferences, and collaborate with partner institutions. 

The role also includes contributing to proposal development, mentoring junior researchers, participating actively in group discussions, and potentially contributing to undergraduate or graduate teaching.

Applicants should hold (or be close to completing) a PhD in computational/theoretical chemistry, physics, or a closely related discipline.

 A strong background in electronic structure methods (e.g., DFT, AIMD), particularly in transition-metal systems, is required, along with experience in molecular simulation and enhanced sampling techniques. Candidates must be proficient in Python and familiar with version control platforms such as GitHub.

You should demonstrate a strong research track record through peer-reviewed publications, the ability to work independently while collaborating effectively across computational and experimental disciplines, and excellent written and oral communication skills. 

The successful candidate will be organised, self-motivated, and committed to contributing positively to a collaborative research environment.

Experience in supramolecular chemistry, metal catalysis, or machine learning in chemistry would be advantageous, as would familiarity with ML approaches for atomistic modelling (e.g., MACE, ACE, NequIP, PhysNet, reactive MD). Prior contributions to scientific code or datasets are also welcomed.

Join an ambitious ERC-funded project and help advance predictive modelling at the interface of supramolecular chemistry, catalysis, and machine learning.

The closing date for applications is 12 noon 18 March 2026. Please upload a CV and supporting statement as part of your online application