Computational Biologist - Metabolic Modelling & Deep Learning

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.

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.

You will join the Bioscience Digital Group within the world's largest agrochemical company. You will work alongside data scientists and bioinformaticians, utilizing Syngenta's high‑performance computing infrastructure and rich proprietary datasets. You will be supervised industrially by Dr. Chris O'Grady, a Senior Principal Scientist in Computational Biology.