There are openings in the Hendricks team for research assistants at all levels including undergraduate, MS, PhD, and post-doc. See below and contact me for more details about other possible positions.
EUReCA! Work-Study Program for undergraduate research assistants at CU Denver (https://www.ucdenver.edu/lynxconnect/undergraduate-research/jobs)
Postdoctoral Fellowship in Biomedical Informatics (https://cu.taleo.net/careersection/2/jobdetail.ftl?job=31649&lang=en)
The Hendricks Lab in the Department of Biomedical Informatics at the University of Colorado Anschutz Medical Campus is seeking a Postdoctoral Fellow with a background in human genomics, biostatistics, statistics, computational biology, bioinformatics, machine learning, and/or related fields. Individuals from diverse backgrounds are strongly encouraged to apply.
The postdoc and PI will jointly envision projects tailored to the fellow’s skillset and long-term career goals. Postdocs will be expected to carry out rigorous statistical, computational, and machine learning research, analyze data and interpret findings, and disseminate findings, software, and data to the biomedical community.
The Hendricks team works at the intersection of biomedical research and statistical/machine learning method development. Our goal is to support rigorous, reproducible, and representative research from basic science to implementation of precision health. Our core activities towards this mission are: 1) building open-source and broadly useful data science methods and software, and 2) promoting growth of a representative research community through programs, mentoring, and collaboration. Recent projects include improving the utility and equity of genetic summary data by detecting and leveraging substructure, identifying metabolomic derived food biomarkers, and developing standards for robust use of common external controls.
Current project funding includes support for methodological and applied work in identifying and leveraging substructure in genomic summary data, employing deep phenotyping and genotyping data to characterize systemic determinants of health, identifying food biomarkers, and characterizing multi-omic drivers of healthy diets.