U
University of Liverpoolvia Indeed
Computational Biologist - Metabolic Modelling & Deep Learning
Liverpool, ENG, GBPosted 4mo ago
ML EngineerMid Level#python
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About the Role
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 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.
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.
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.
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.
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