Machine Learning Engineer

JIJ develops advanced computational technologies for complex scientific and industrial problems, including machine learning and computational life sciences. The Machine Learning Engineer will develop next-generation models of longitudinal microbiome systems using graph machine learning, temporal modelling, biologically informed learning, multi-omics integration, and interpretable or causal methods. The role also involves translating research into reproducible experiments, scientific software, technical reports, and publications.

Responsibilities:

• Develop computational models for microbiome dynamics, perturbation, recovery, resilience, and broader biological network problems
• Build biologically meaningful graph and multi-omics representations that integrate microbial, genomic, metabolic, pathway, phylogenetic, and host information
• Formulate biologically meaningful questions and translate them into rigorous computational experiments
• Design and evaluate GNN, graph-transformer, temporal, causal, and interpretable learning methods
• Address microbiome-specific challenges such as compositionality, sparsity, batch effects, confounding, and irregular sampling
• Establish strong classical and statistical baselines and distinguish statistical associations from biologically plausible mechanisms
• Translate state-of-the-art research into reproducible experiments, research software, technical reports, and peer-reviewed publications
• Collaborate across computational and experimental teams and with academic, scientific, and industry partners
• Develop reusable methods and technologies rather than one-off analyses wherever possible