Join the University of Liverpool's cutting-edge research team as a Computational Biologist, leveraging metabolic modelling and deep learning to advance biological discoveries and drive innovative healthcare solutions.

Overview

The University of Liverpool is seeking a talented Computational Biologist to join our Institute of Systems, Molecular and Integrative Biology. This role focuses on developing and applying advanced metabolic modelling techniques integrated with deep learning to unravel complex biological pathways and predict metabolic phenotypes.

Key Responsibilities

• Develop and refine genome-scale metabolic models for microbial and human systems
• Implement deep learning algorithms to integrate multi-omics data for model parameterization and validation
• Collaborate with experimental biologists to design and interpret validation experiments
• Analyze large-scale datasets from metabolomics, transcriptomics, and proteomics
• Contribute to grant writing, publications, and presentations at international conferences
• Mentor junior researchers and students

Research Environment

You'll work in a vibrant, interdisciplinary environment with access to world-class facilities, including high-throughput sequencing, mass spectrometry, and GPU clusters for AI computations. This position offers the chance to contribute to high-impact projects funded by UKRI and EU grants, with real-world applications in personalized medicine, synthetic biology, and drug discovery.

Why Liverpool?

Liverpool is a hub of innovation with a rich academic heritage. The University provides excellent support for career growth in academia or industry.

• PhD in Computational Biology, Bioinformatics, Systems Biology, or a related field
• Proven expertise in metabolic modelling (e.g., Flux Balance Analysis, constraint-based modelling)
• Strong experience with deep learning frameworks (e.g., TensorFlow, PyTorch) applied to biological data
• Proficiency in Python, R, or MATLAB for scientific computing and data analysis
• Familiarity with omics data (genomics, metabolomics, proteomics)
• Excellent problem-solving skills and ability to work in interdisciplinary teams
• Strong publication record in peer-reviewed journals
• Experience with high-performance computing or cloud platforms is advantageous

• Competitive salary with annual progression opportunities
• 30 days annual leave plus bank holidays
• Generous pension scheme (USS)\n- Access to state-of-the-art research facilities and computational resources
• Opportunities for professional development and conference attendance
• Flexible working arrangements and family-friendly policies
• On-campus gym, childcare facilities, and staff discounts