Postdoctoral Researcher, AI for Multi-Omics Data Integration

About the role

CBS is seeking a Postdoctoral Researcher in Artificial Intelligence and Data Science with expertise in multi-omics data integration for health and precision medicine. The researcher joins a multidisciplinary team developing AI-driven approaches to integrate and analyse genomics, transcriptomics, proteomics, metabolomics and microbiome datasets, looking for biomarkers, therapeutic targets and mechanistic insights into complex diseases.

Key responsibilities

• Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning.
• Develop deep learning architectures such as variational autoencoders, graph neural networks and transformers for cross-omics representation and feature extraction.
• Apply multi-view learning, transfer learning and data fusion to combine heterogeneous omics datasets with clinical metadata.
• Conduct network-based analysis (gene regulatory, protein-protein interaction and metabolic networks) to identify disease drivers and biomarkers.
• Build predictive models for disease classification, patient stratification and treatment response.
• Collaborate with biologists, clinicians and bioinformaticians to interpret and validate computational findings.
• Publish results in high-impact journals and present at conferences, and mentor graduate students and early-career researchers.

Required profile

• Ph.D. in Bioinformatics, Computational Biology, Data Science, Artificial Intelligence or a related field.
• Proven experience in multi-omics data integration and analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome).
• Strong publication record in peer-reviewed journals.
• Excellent communication skills and ability to work in a multidisciplinary environment.

Required skills

• Machine learning, deep learning and AI frameworks such as TensorFlow, PyTorch and Scikit-learn.
• Bioinformatics tools and databases (Bioconductor, Galaxy, KEGG, Reactome, STRING).
• Python, R and Unix/Linux; knowledge of network inference, causal modelling and graph-based AI.
• Multi-modal data fusion and representation learning.
• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and HPC; understanding of data privacy and ethics in clinical data.

Applications consist of a single PDF containing a cover letter, a detailed CV with publications and contact details for two academic referees.