Research Associate in AI-Enhanced Molecular Dynamics to study Antibiotic Accumulation in Bacteria

Location: White City Campus

About the role:

Supported by funding from the Wellcome Trust, this project offers an exciting opportunity for a Research Associate to join the groups of Prof Ramon Vilar (Chemistry) and Prof Mauricio Barahona (Mathematics) at Imperial College London. You will work at the interface of computational chemistry and data science, to investigate the structure, dynamics, and transport properties of membrane proteins. The role is part of the Gram-Negative Antibiotic Discovery Innovator (Gr-ADI) initiative, a major collaborative effort aimed at addressing antimicrobial resistance (AMR), which represents a significant and growing global health threat

The research will focus on understanding how small molecules cross and accumulate within Gram-negative bacteria such as Klebsiella pneumoniae, which present significant challenges due to their dual membranes and efficient efflux mechanisms. By taking a systematic, data-driven approach, you will contribute to uncovering the fundamental principles governing molecular permeation and intracellular accumulation. Given the limited understanding of how to develop antibiotics that can cross these membranes and accumulate within bacteria, we will take a systematic, data-driven approach to elucidate the chemical ‘rule book’ that determines small molecule penetration and accumulation in Klebsiella to guide rational design of next generation antibiotics.

What you would be doing:

You will contribute to the development and application of molecular dynamics (MD) simulations, integrated with machine learning (ML) approaches, to study the structure, dynamics, and transport properties of membrane permeation systems, including porins and efflux channels, in Gram-negative bacteria. A key objective of the role is to develop automated feature-generation pipelines that incorporate simulation-derived biophysical descriptors into broader ML frameworks for predicting bacterial drug accumulation. This position forms part of a large, multi-disciplinary effort to elucidate the principles governing small-molecule accumulation in Klebsiella and other Gram-negative pathogens, and to develop open AI/ML-based models that support the rational design of next-generation antibiotics.

You will be contributing to a dynamic and ambitious global project – the Gr-ADI initiative, funded by Wellcome Trust, that involves several groups around the world. At Imperial, you will work closely with a multidisciplinary team, across assay design, small molecule screens and chemical biology and microbiology (Prof Ed Tate, Prof Gad Frankel, Dr Andy Edwards), ‘omics technologies (Dr Matt Child), data science (Dr Marko Storch) and AI (Prof. Alessandra Russo, Prof. Mauricio Barahona).

What we are looking for:

Essential criteria:

• Hold a PhD (or be near completion) in computational chemistry, computational biology, or a related discipline.
• Experience in molecular dynamics simulations and structural bioinformatics.
• Experience in ML / data-driven modelling such as machine-learned coarse-grained MD, or similar, including relevant frameworks
• Experience of effective collaboration and undergraduate/postgraduate supervision.

What we can offer you:

• Become part of a multidisciplinary team working on one of the major challenges in antibiotic drug discovery.
• The opportunity to develop your research career at a world-leading institution and be part of our mission to continue science for humanity.

Further Information

This is a full-time, fixed-term role for up to 24 months, with the possibility of an extension.

Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.

The position is on-site at White City Campus.

For further details please contact: Prof Ramon Vilar - r.vilar@imperial.ac.uk

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