Indiana University
Overview
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Postdoctoral Researcher
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Indiana University Title: Postdoctoral Researcher Appointment Status: Non-Tenure Track Department: IU Indianapolis Department of Biomedical Engineering and Informatics – Luddy School Location: Indianapolis 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 design and implement impactful scientific approaches to analyzing human biomedical datasets and drive discoveries in public health to uncover novel mechanisms and insights into heart health and disease, with a focus on addressing significant health disparities that are understudied, underrepresented, and underreported (U3) in current research.
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), 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 data analysis skills using bioinformatics methods (e.g., R/Bioconductor) or machine learning tools (e.g., Python with sklearn, TensorFlow, PyTorch) to improve understanding of cardiovascular disease phenotypes and related co-morbidities such as kidney disorders and type 2 diabetes.
Comfort writing code in R or Python for bioinformatics/statistical analysis or proficiency in Unix shell scripting or high-performance computing.
Familiarity with or prior experience with NGS analysis tools such as PLINK, or experience in a cloud computing environment or UNIX/Linux/HPC cluster.
Contact Department Contact for Questions: Dr. Bohdan Khomtchouk at bokhomt@iu.edu
Additional Information Salary and Rank: Not specified in this excerpt
Posting Date: 02/09/2024
Expected Start Date: 03/01/2024
Posting Number: IU-100722-2024
Job Functions: Research, Analyst, and Information Technology
Industry: Higher Education
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Join to apply for the
Postdoctoral Researcher
role at
Indiana University Title: Postdoctoral Researcher Appointment Status: Non-Tenure Track Department: IU Indianapolis Department of Biomedical Engineering and Informatics – Luddy School Location: Indianapolis 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 design and implement impactful scientific approaches to analyzing human biomedical datasets and drive discoveries in public health to uncover novel mechanisms and insights into heart health and disease, with a focus on addressing significant health disparities that are understudied, underrepresented, and underreported (U3) in current research.
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), 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 data analysis skills using bioinformatics methods (e.g., R/Bioconductor) or machine learning tools (e.g., Python with sklearn, TensorFlow, PyTorch) to improve understanding of cardiovascular disease phenotypes and related co-morbidities such as kidney disorders and type 2 diabetes.
Comfort writing code in R or Python for bioinformatics/statistical analysis or proficiency in Unix shell scripting or high-performance computing.
Familiarity with or prior experience with NGS analysis tools such as PLINK, or experience in a cloud computing environment or UNIX/Linux/HPC cluster.
Contact Department Contact for Questions: Dr. Bohdan Khomtchouk at bokhomt@iu.edu
Additional Information Salary and Rank: Not specified in this excerpt
Posting Date: 02/09/2024
Expected Start Date: 03/01/2024
Posting Number: IU-100722-2024
Job Functions: Research, Analyst, and Information Technology
Industry: Higher Education
#J-18808-Ljbffr