Description

• Interest in analyzing biomedical/clinical/genomics datasets using computational approaches such as longitudinal analysis, mixed-effect modeling, regression, and AI/machine learning in large-scale electronic health records-based biological databases and biobanks such as UK Biobank, NIH All of Us Research Program, etc.
• Interest in gaining hands-on experience working with very large human datasets, social determinants of health, and integrative bioinformatics strategies, with a special focus on AI/machine learning approaches to derive new clinical insights at scale.
• Basic working knowledge or publication track record in either: next-generation sequencing (NGS) methods, biostatistics methodologies, genetic/molecular epidemiology, and/or bioinformatics or computational biology approaches/pipelines is required.
• Interest in implementing cutting-edge bioinformatic methods for large-scale population genetics studies, such as genetic association studies (GWAS), quantitative trait loci (QTL) colocalization, fine-mapping, (poly-)genetic risk prediction, pleiotropy analysis, and Mendelian randomization.
• Previous experience working with large-scale biomedical datasets (e.g., RNA-seq, ChIP-seq, single cell-seq, ATAC-seq, genotype data, biomarkers, etc.) is required.
• Desire to apply your skills in data analysis to utilize bioinformatics methods (e.g., from R/Bioconductor) or machine learning tools (e.g., using Python packages such as sklearn, TensorFlow, PyTorch, etc.) to improve our understanding of the biology of cardiovascular disease phenotypes, including associated renal and metabolic co-morbidities such as kidney disorders and type 2 diabetes.
• Comfortable writing code in languages like R or Python for bioinformatics/statistical analysis or proficiency in Unix shell scripting or high-performance computing.
• Familiarity/interest with or prior experience working with NGS algorithms such as PLINK, or experience working in a cloud computing environment or UNIX/linux/HPC cluster.