Computational Biologist – Metabolic Modelling & Deep Learning KTP Associate

Computational Biologist – Metabolic Modelling & Deep Learning KTP Associate Biochemistry, Cell and Systems Biology
113893 £ 45,000 - £48,000 pa13-Aug-2026 23:30

Hours of work:
Full-time Tenure:
Fixed term for 2 years

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.

The role involves developing a species-agnostic computational pipeline that integrates cutting-edge deep learning methods—such as Alpha

Fold2 and Fold Seek—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.

You 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.

Our commitment to Equality, Diversity and Inclusion We are committed to enhancing a workforce as diverse as our community and particularly encourage applicants who are of minoritised genders and ethnic backgrounds, living with a disability, and/or are members of the LGBTQIA+ community.