Scientist

Perform advanced analysis of NGS and multi-omics datasets, including WGS, targeted sequencing, RNA-seq, and long-read sequencing.Develop, maintain, and optimize automated bioinformatics pipelines and workflows, incorporating AI/ML approaches where appropriate.Apply machine learning and advanced analytics to variant prioritization, disease association, and multi-omics data integration.Curate gene, variant, and disease knowledgebases and support their integration into research and clinical workflows.Collaborate with bioinformaticians, other scientists, clinicians, and biologists to translate complex data into actionable insights.Contribute to grant applications, research publications, and high-impact scientific outputs.Present research findings at national and international conferences.Participate in teaching and education-related activities within the College of Medicine, MBRU.Minimum qualification:At least 2 years of post-doctoral or equivalent training experience.Strong programming skills in Python and/or R (Java/Perl an advantage).Familiarity with Linux/UNIX systems and shell scripting.Excellent understanding of NGS data processing, alignment, variant calling, annotation, and quality control.Experience applying machine learning or AI methods to genomics or multi-omics data (e.g., classification, clustering, predictive modelling).Knowledge of sequencing platform–specific biases, genome structure, and data interpretation.Hands-on experience with large-scale genomic datasets, including WGS, WES, RNA-seq, targeted and amplicon sequencing.Experience working in HPC, cluster, and/or cloud computing environmentsFamiliarity with genomic databases and resources such as UCSC Genome Browser, gnomAD, and NCBI.Solid foundation in applied biostatistics and statistical interpretation of genomic data.#J-18808-Ljbffr