Machine Learning Engineer (Computational Biology)

jij • United Kingdom

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AI Summary

Develop next-generation AI models for longitudinal microbiome systems and biological network modelling. Design and evaluate graph machine learning, temporal, and causal models to solve complex life-science problems. Requires a PhD or research-intensive MSc in a relevant field with expertise in ML and computational biology.

Key Highlights

Focus on representing biological systems as dynamic graphs and multi-omics representations

Translate state-of-the-art research into reproducible scientific technology and publications

Interdisciplinary role bridging biology, mathematics, and machine learning

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Key Responsibilities

Develop computational models for microbiome dynamics, perturbation, recovery, and resilience.

Build biologically meaningful graph and multi-omics representations integrating genomic, metabolic, and phylogenetic information.

Design and evaluate GNN, graph-transformer, temporal, causal, and interpretable learning methods.

Address microbiome-specific challenges such as compositionality, sparsity, and batch effects.

Translate research into reproducible experiments, software, technical reports, and peer-reviewed publications.

Collaborate across computational and experimental teams and with academic and industry partners.

Technical Skills Required

Machine Learning Python Graph Machine Learning

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Benefits & Perks

Annual Salary: £65,000–£90,000

Stock options

Visa sponsorship

Relocation support

Remote-first working style

Flexible working hours

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Nice to Have

Experience with longitudinal microbiome or host-microbiome modelling

Expertise in neural differential equations, state-space models, or causal inference

Experience with QIIME 2, DADA2, Bioconductor, MetaPhlAn, PyTorch Geometric, or DGL

Knowledge of quantum or quantum-inspired methods

Production-quality scientific software development or open-source contributions

Job Description

Develop the Next Generation of AI for Computational Life Sciences

At JIJ, we develop advanced computational technologies for solving complex scientific and industrial problems. Our R&D spans machine learning, mathematical optimisation, network science, quantum and quantum-inspired computing, with a strong emphasis on translating fundamental research into practical scientific impact.

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