Computational Biologist – Metabolic Modelling & Deep Learning

The role involves developing a species-agnostic computational pipeline that integrates cutting-edge deep learning methods – such as AlphaFold2 and FoldSeek – to enhance protein function prediction and estimate kinetic parameters for enzyme-constrained models. By processing proprietary multi-omics data, you will initially focus on determining metabolic vulnerabilities in Zymoseptoria tritici before expanding the methodology to other species to support global food security.

Candidates should have a PhD in Computational Biology, Bioinformatics, or a related quantitative field, with proficiency in Python and a strong foundation in genome-scale metabolic modelling. This fixed-term post is available for 24 months.

The University of Liverpool and Syngenta Limited have established a Knowledge Transfer Partnership (KTP) to recruit a specialized Computational Biologist to join Syngenta’s global R&D hub in Jealott’s Hill. This project aims to revolutionize crop protection discovery by implementing a “biology-first” approach to pathogen analysis, focusing on the development of novel methods for constructing genome-scale metabolic maps of commercially relevant pathogens. You will lead the preparation and publication of high-impact scientific papers while embedding these workflows into Syngenta’s target identification framework.