Title: UH & IU joint bioinformatics and computational biology internship
Lab: Khomtchouk Lab & Ola HAWAII
Location: University of Hawai’i & Indiana University
Job Description: Develop new quantitative genetics and computational genomics approaches to analyze human biomedical data and find novel biological mechanisms and insights into the genetic heterogeneity of cardiovascular/renal/metabolic diseases within diverse historically underrepresented populations, with a focus on rare and common forms of diabetes and its complications in Native Hawaiian and Pacific Islander populations.
Position Type: Remote/hybrid
Status: Successful applicants may receive research class credits (if applicable) and/or research mentorship/publication opportunities; open to undergraduate students, graduate students (MSc and PhD), postdocs, visiting scholars, medical residents, and fellows.
Relevant Publications:
Relevant Press:
Preferred Qualifications:
Skills
- Interest in learning how to implement cutting-edge precision medicine and biomedical data science approaches in computational biology and bioinformatics studies (genetic association studies (GWAS), quantitative trait loci (QTL) colocalization, fine-mapping, (poly-)genetic risk prediction, pleiotropy analysis, Mendelian randomization, etc.).
- Basic working knowledge of either: next-generation sequencing (NGS) methods, biostatistics, genetic/molecular epidemiology, public health, quantitative genetics, and/or bioinformatics or computational biology pipelines is preferred — but not strictly required.
- Interest in gaining hands-on experience working with human genetics datasets, social determinants of health, and integrative bioinformatics strategies, with a special focus on causal inference methods to predict potentially new clinical insights in the context of cardiovascular/renal/metabolic diseases.
- Interest in analyzing diverse and highly admixed human genomics datasets using analytical approaches such as: longitudinal analysis, mixed-effect modeling, regression, classification, and AI/machine learning algorithms applied to large-scale electronic health records-based biobanks and population health databases (e.g., UK Biobank, NIH All of Us Research Program, dbGaP, etc.).
- Previous experience (or just general interest in) working with deeply phenotyped/sequenced biomedical datasets (e.g., RNA-seq, ChIP-seq, single cell-seq, ATAC-seq, genotype data, biomarkers, etc.).
- Desire to apply your data science skills to deploy existing 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 cardiometabolic disease phenotypes, including associated renal and vascular co-morbidities such as kidney disorders and heart disease.
- Comfortable writing code in languages like R or Python for biomedical informatics, statistical analyses, or proficiency in Unix shell scripting and/or high-performance computing is a plus.
- Familiarity/interest with NGS algorithms such as PLINK, or experience working in a cloud computing environment or UNIX/linux/HPC cluster, are a plus but not explicitly required.
For project details — if you are interested in this opportunity and would like to receive further information, please forward your CV/resume and a brief description (email or cover letter) of your research interests/qualifications to Dr. Bohdan Khomtchouk at: bokhomt@iu.edu