Postdoctoral Researcher in Artificial Intelligence (AI) and Data Science

data integration for health and precision medicine. The successful candidate will join a multidisciplinary team developing AI-driven approaches to integrate and analyze genomics, transcriptomics, proteomics, metabolomics, and microbiome datasets to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project addresses critical challenges in personalized medicine, disease stratification, and multi-modal data fusion, enabling next-generation solutions in precision health and biomedical research. Scientific Challenges Addressed in the Position: Heterogeneity and high dimensionality of multi-omics data requiring advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease progression modeling. Explainability and interpretability of AI models to support clinical decision-making and regulatory compliance in healthcare settings. Scalability and computational efficiency in processing and integrating massive multi-omics datasets from clinical cohorts. Key Responsibilities: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view learning, transfer learning, and data fusion techniques to integrate heterogeneous omics datasets and clinical metadata. Conduct network-based analysis (gene regulatory networks, protein-protein interaction networks, metabolic networks) to identify key disease drivers and biomarkers. Build predictive models for disease classification, patient stratification, and treatment response prediction. Collaborate with biologists, clinicians, and bioinformaticians for data interpretation and validation of computational findings in clinical or experimental settings. Disseminate research outcomes through publications in high-impact journals, conference presentations, and workshops. Mentor and support the training of graduate students and early-career researchers in AI and multi-omics integration. Required Qualifications: Ph.D. in Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, or a related field. Proven experience in multi-omics data integration , omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning , deep learning , and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome, STRING). Proficiency in Python , R , and Unix/Linux-based environments for high-performance data analysis. Knowledge of biological network inference , causal modeling , and graph-based AI approaches . Experience in multi-modal data fusion , representation learning , and heterogeneous data integration . Strong publication record in relevant peer-reviewed journals. Excellent communication skills and ability to work in a multidisciplinary environment. Familiarity with cloud-based computing platforms (AWS, Azure, Google Cloud) and high-performance computing (HPC) environments. Understanding of data privacy , security , and ethical considerations in handling clinical data. Application Process: Interested candidates should submit the following documents in a single PDF: A cover letter outlining their research interests, motivation, and relevant experience. A detailed Curriculum Vitae (CV) with a list of publications. Contact details of two academic referees .