Indiana University
Position Summary
Are you passionate about genomics, big data, drug discovery, and AI/machine learning? Interested in advancing cutting-edge multi-omics research to explore genetic and biomolecular mechanisms underlying heart disease, with the ultimate goal of contributing to innovative treatment strategies? Apply now to make a difference by designing and implementing new impactful scientific approaches to analyzing human biomedical datasets and driving discoveries in public health to uncover novel biological mechanisms and insights into heart health and disease, with a focus on addressing significant health disparities that are often understudied, underrepresented, and underreported (U3) in current research. Basic Qualifications
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. Department Contact for Questions
Dr. Bohdan Khomtchouk at:
bokhomt@iu.edu
#J-18808-Ljbffr
Are you passionate about genomics, big data, drug discovery, and AI/machine learning? Interested in advancing cutting-edge multi-omics research to explore genetic and biomolecular mechanisms underlying heart disease, with the ultimate goal of contributing to innovative treatment strategies? Apply now to make a difference by designing and implementing new impactful scientific approaches to analyzing human biomedical datasets and driving discoveries in public health to uncover novel biological mechanisms and insights into heart health and disease, with a focus on addressing significant health disparities that are often understudied, underrepresented, and underreported (U3) in current research. Basic Qualifications
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. Department Contact for Questions
Dr. Bohdan Khomtchouk at:
bokhomt@iu.edu
#J-18808-Ljbffr