Head of Metabolic Analysis and Modelling - Plant Biology Institute

Head of Metabolic Analysis and Modelling - Plant Biology Institute

EIT - Ellison Institute of Technology

Oxford, United Kingdom

Your Role:

At EIT we are seeking an accomplished leader to serve as Head of Metabolic Analysis and Modelling for the Plant Biology Institute. This pivotal role will shape a newly established and rapidly scaling Plant Biology Institute that aspires to become a global leader in delivering scientific breakthroughs with real-world impact.

The successful candidate will establish and lead PBI’s metabolic analysis, delivering analytical capabilities and workflows to support PBI’s trait and technology research groups.

This role requires close collaboration with research scientists working across a broad range of projects including plant and microbial metabolic engineering, plant breeding, plant phenotyping. The role will require working with other institutional stakeholders to scope, design, equip, and operate a new function within the research institute.

Your Responsibilities:

• Lead and develop the Institute’s metabolic analysis, delivering robust, scalable analytical frameworks to provide metabolite and metabolic modelling analysis to support plant biology research programmes.
• Define and implement a strategic vision for integrative metabolic analysis that aligns with the Institute’s scientific priorities and long-term goals.
• Drive the development and application of predictive models of plant metabolism, resource use, and productivity to inform experimental design and technology development.
• Design, implement, and maintain advanced analysis workflows and pipelines for the integration and analysis of multi-omics and high-throughput biological datasets.
• Develop reproducible, well-documented, and version-controlled analysis frameworks, supporting transparency, open science, and collaborative research.
• Ensure analytical platforms are robust, scalable, and interoperable, supporting both exploratory research and translational applications.
• Remain at the forefront of emerging methods in metabolic analysis, metabolic modelling, machine learning, and data-driven biology, identifying opportunities to apply new tools to accelerate discovery.
• Work closely with experimental scientists, Group Leaders, and technology teams across all of EIT to co-develop analytical approaches that directly support experimental programmes and strategic research questions in PBI.
• Translate complex computational outputs into clear biological insights, enabling informed decision-making across research and translational activities.
• Build sustainable capability in metabolic analysis, metabolic modelling and integrative analysis, fostering a culture of scientific excellence, collaboration, and continuous innovation.
• Manage resources - including personnel, equipment, and budgets - in coordination with the senior leadership team.

Requirements

Essential Skills, Qualifications & Experience:

• Advanced degree (PhD or equivalent experience) in metabolic analysis, systems biology, computational biology, bioinformatics, data science, applied mathematics, or a related field.
• Substantial experience leading metabolic analysis or modelling functions within a research-intensive or scientific environment.
• Proven expertise in metabolic modelling, constraint-based modelling, systems biology, or integrative multi-omics analysis.
• Strong technical background in programming and data engineering (e.g. Python, Linux environments, workflow management systems, databases, APIs).
• Experience designing and delivering scalable, end-to-end analytical solutions for complex biological datasets.
• Demonstrated ability to collaborate effectively across disciplines, translating computational outputs into actionable biological and strategic insight.
• Track record of building, mentoring, and leading high-performing teams.
• Excellent communication and stakeholder engagement skills, with the ability to articulate the strategic value of modelling and analytics to diverse audiences.