Looking for a creative candidate with an interest in developing and applying genetic epidemiological and population-based methods to study how polygenic variation impacts human health and disease. Focus areas include evaluating polygenic predictors of disease risk or outcomes, identifying opportunities to translate genetics into clinical practice and  identifying metabolomic, proteomic and other -omic biomarkers related to disease. The lab’s approaches typically include applying discovery-oriented and hypothesis-oriented methodologies in genotyped populations derived from epidemiological and clinically-derived data sets, as well as resources such as All of Us and the UK Biobank. 

Responsibilities would include developing and validating phenotypes, performing genetic quality control on data sets, constructing polygenic predictors, and performing analyses using a range of genetic methods. Essential skills include an understanding of epidemiological/statistical methods and an ability to work with large high-dimensional data sets within linux and cloud-based analytical environments. A clinically-oriented or biological background with experience in genetic research is preferred. Excellent written and verbal communication skills are needed to assist with presenting data and manuscript development. Experience with statistical packages such as R as well as genetic programs such as PLINK. 

Education
PhD in Computer Science or a related field of biological science, with thesis work in bioinformatics and computational biology or
Master's Degree in Computer Science or a related field of biological science or
Bachelor's Degree in Computer Science or a related field of biological science

Experience
2 years of related research experience in bioinformatics and computational biology with Master's Degree or
4 years of related research experience in bioinformatics and computational biology with Bachelor's Degree.