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Machine Learning Engineer (Computational Biology)

jij • United Kingdom
Visa Sponsorship Relocation
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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
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
Benefits & Perks
Annual Salary: £65,000–£90,000
Stock options
Visa sponsorship
Relocation support
Remote-first working style
Flexible working hours
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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