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Post-Doctoral Research Associate - Computational Biologist

Oakland University Rochester Hills, MI

  1. Salary Not stated by the employer
  2. Employer Oakland University
  3. Location Rochester Hills, MI
  4. Workplace On site
  5. Role Research Associate
  6. Posted Mar 18, 2025
  7. Reference pa-34235
Post-Doctoral Research Associate - Computational Biologist at Oakland University, Rochester Hills, MI
The Oakland University Institute for Data Science, in conjunction with scientists at the Universityof Michigan and Harvard have recently formed a collaborative American Heart Association funded Strategically Focused Research Network (https://newsroom.heart.org/news/researchers-awarded-15-million-to-study-inflammations-impact-on-heart-brain-health). This network has been established to focus on the interrelated domains of heart and brain health and aims to bring participating researchers together to unravel the biological mechanisms that the heart and brain have in common, as well as how these organs signal to each other and impact clinical outcomes. Despite the recognized interconnectedness of these health areas, especially in relation to metabolic, neurodegenerative, neurological, and neurocognitive disorders, few, if any, platforms offer integrated research solutions in this space. The role of the Institute for Data Science is to provide a computational platform that will be the heart of data analytics for this research network.

We are searching for a highly motivated and computationally skilled individual to join the team interested in harnessing, merging, and sharing data, algorithms, and computational resources to discover innovative insights, launch clinical and translational research studies, and evaluate treatments arising from our collaborative research efforts that relate heart and brain health and disease(s). The selected candidate will focus on methods for analyzing diverse data types and integrating 'omics expression data, imaging data, and wearable sensors data. The data types included will range from patient-specific to aggregated data, spanning the continuum of genetics, multi-omics, metabolomics, physiologic/wearable data, tissue, organ, imaging data, and patient-specific longitudinal electronic health record (EHR) data, including relevant Social Determinates of Health (SDOH) and geospatially encoded demographic and health outcomes data.
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