King's College London

About us

The Faculty of Natural, Mathematical & Engineering Sciences (NMES) comprises Chemistry, Engineering, Informatics, Mathematics, and Physics – all departments highly rated in research activities and a wide-ranging portfolio of education programmes. 

Celebrating diversity and supporting staff is important to us and we offer a range of provision including flexible working, caring support (including a Parenting and Carers Fund and the Carer’s Career Development Fund), training, and a variety of diversity and inclusion networks. Staff can apply for flexible working to help them balance the demands of their professional and personal commitments and we offer comprehensive leave policies for parental, adoption, surrogacy, dependant and shared leave.

The university is making investment in NMES, and both student and staff numbers are growing. Our staff come from over 45 countries and 56% of our students are from outside the UK. Further details available at www.kcl.ac.uk/nms. 

About the role

Applications are invited for a 2.5‑year fixed-term position in the group of Dr Francisco Martin‑Martinez in the Department of Chemistry, King’s College London, in co-supervision with Dr. Micaela Matta. 

We are seeking a postdoctoral research associate to contribute to an innovative EU Pathfinder project at the intersection of polymer chemistry, molecular dynamics simulations, and machine learning. The primary goal is to develop an in-silico framework to accelerate the discovery of novel Polyhydroxyalkanoate (PHA) polymers for more sustainable food packaging and to understand their end-of-life degradation mechanisms. The project will be conducted in close collaboration with experimental partners in the project consortium led by AINIA research centre in Spain. 

The postholder will have access to state‑of‑the‑art high‑performance computing (HPC) resources, will be embedded within the Net Zero Centre and the King’s institute for AI, and will work alongside experts in computational and experimental chemistry.

Applicants should have a PhD in Chemistry or a related field, with a strong background in atomistic molecular dynamics simulations of soft mater, especially polymers and/or proteins, and demonstrable experience or strong interest in machine‑learning approaches for accelerating molecular modelling and the prediction of chemical properties.

For informal enquiries, please contact Dr Martin‑Martinez at francisco.martin-martinez@kcl.ac.uk. This post is advertised with an intended start date as soon as possible.

About you

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

1.      A PhD in Chemistry, Physics, Materials Science, or a related discipline.

2.      Evidence of the ability to contribute to writing and publishing research papers in peer‑reviewed journals.

3.      Proven research background in atomistic molecular dynamics simulations, ideally of polymers and/or proteins, for structure–property prediction and molecular design.

4.      Strong experience with python programming for molecular simulation and cheminformatics.

5.      Experience with version‑controlled GitHub repositories and Unix/Linux‑based high‑performance computing environments.

6.      Good communication skills, including the ability to write scientifically, clearly, and succinctly, and to present research to both specialist and non‑specialist audiences.

Desirable criteria

1.      Experience with machine‑learning interatomic potentials and their deployment in large‑scale molecular dynamics simulations.

2.      Experience working in multicultural and international research environments, with a track record of international collaborations.

3.      Research experience with modelling molecular and/or materials degradation (e.g., reactive MD, conceptual DFT).

4.      Experience with AI/ML for molecular/materials discovery and property prediction.

Downloading a copy of our Job Description

Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process.

* Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.

Further Information

We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.

We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's.

As part of this commitment to equality, diversity and inclusion and through this appointment process, it is our aim to develop candidate pools that include applicants from all backgrounds and communities.

We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

To find out how our managers will review your application, please take a look at our ‘ How we Recruit’ pages.