Machine Learning Engineer

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.

We are expanding our interdisciplinary life-sciences research team to develop advanced computational approaches for challenging problems in microbiology, bioinformatics, genetics, microbiome science, and biological network modelling
. Example problems include understanding how microbial communities change over time, how microorganisms interact, how biological perturbations propagate through these communities, and which mechanisms determine recovery or loss of resilience.

This is not a role focused simply on applying standard machine-learning pipelines to biological datasets. It is particularly suited to researchers interested in developing new computational methodology and translating research ideas into reproducible scientific technology.

We are looking for a Machine Learning Engineer with expertise spanning computational biology and machine learning to develop next-generation models of longitudinal microbiome systems.

You will work with high-dimensional biological datasets including microbial abundance, genomic and functional information, environmental or intervention metadata, and potentially metabolomic and other multi-omics measurements. A central research direction will be representing biological systems as dynamic graphs
, where microorganisms, genes, metabolites, pathways, or host factors form interacting networks whose topology and state evolve over time.

You will investigate state-of-the-art approaches including:

• Graph machine learning
  : GNNs, message passing, graph attention, graph transformers, and heterogeneous or knowledge-graph models.
• Temporal and dynamic modelling
  : evolving interaction networks and continuous-time or state-space approaches.
• Biologically informed learning
  : combining pathway, phylogenetic, metabolic, and genomic priors with data-driven models.
• Representation and multi-omics learning
  : self-supervised methods, foundation-model representations, and multimodal integration.
• Causal, interpretable, and uncertainty-aware modelling for perturbation analysis, biological discovery, and robust prediction.

Where scientifically justified, you may also investigate quantum or quantum-inspired approaches (e.g. quantum machine learning, quantum kernels, tensor networks, Ising/QUBO formulations). These are complementary research directions rather than mandatory components of every project.

• 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.

We are looking for engineers and researchers who can move comfortably between biology, mathematics, and machine learning
. You may be an excellent fit if you have:

• A PhD, or a research-intensive MSc with significant experience, in Computer Science, Machine Learning, Bioinformatics, Computational Biology, Microbiology, Genetics, Genomics, Systems Biology, or a closely related discipline.
• A strong background in statistics and machine learning, with the ability to design, train, validate, and rigorously benchmark predictive models for complex scientific datasets.
• Practical experience…