Reticular chemistry post-doc position available

A post-doctoral research assistant/associate position is available at the University of Cambridge in the Computational Modelling Group under the supervision of Prof. Markus Kraft. The successful candidate will join a multi-disciplinary team developing intelligent data-driven methods for the digital discovery of reticular materials. To date, the team has mined the literature to create a database of materials, developed data structures to describe their Lego-like nature, designed algorithms that exploit the data structures to predict the existence of new materials, implemented workflows for calculating the properties of materials, and used large language models to extract synthesis knowledge from the literature and infer the steps required to synthesise new materials. The vision of the team is to automate materials discovery and synthesis by tightly integrating knowledge engineering, computational modelling, various machine learning techniques (e.g. Chemprop, etc.) and artificial intelligence to design materials with properties tuned for specific applications. The work forms part of The World Avatar (TWA) project, a disruptive approach pioneered by Prof. Kraft to leverage knowledge-based technology to share data and create interoperability across different technical and social domains.

Appointment at Research Associate level is dependent on having a PhD; those without a PhD will be appointed at Research Assistant level. Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded.

The duration of the contract is 12 months in the first instance, with the possibility of being extended following satisfactory performance.

Applicants must have:

1. A Master’s or PhD degree or equivalent experience in a relevant science or engineering discipline (essential).
2. Excellent oral and written communication skills (essential).
3. An interdisciplinary and collaborative research approach (essential).
4. The ability to work as part of a dynamic, multidisciplinary team (essential).
5. A developing bibliography of publications in a relevant research area (desirable).
6. Experience in quantum chemistry, molecular dynamics or other aspects of computational chemistry (desirable).
7. Experience in machine learning approaches for chemistry, including cheminformatics (e.g. familiarity with RDKit and state-of-the-art models, such as graph neural networks) (desirable).
8. Experience in software engineering using Python, Java, JavaScript, or other languages (desirable).