Post-Doctoral Research Associate - Computational Biologist

Oakland University

Rochester (MI)

On-site

USD 50,000 - 65,000

Full time

14 days+

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Job summary

A university in Michigan is seeking a Post-Doctoral Research Associate - Computational Biologist to analyze diverse health data types and support clinical research related to heart and brain health. The ideal candidate has a recent PhD, strong data analysis skills, and knowledge of next generation sequencing. This role is essential for advancing research that links heart and brain health, aiming to discover innovative insights in a collaborative environment.

Qualifications

Recent PhD in computational biology or related field.
Deep understanding of next generation sequencing data.
Experience in multi-omics data analysis.

Responsibilities

Analyze diverse data types and integrate 'omics expression data.
Launch clinical and translational research studies.
Evaluate treatments from collaborative research efforts.

Skills

Next generation sequencing data

Bioinformatics algorithms

Data analysis best practices

Machine learning

Statistical methods

Education

PhD in Computational Biology, Statistics, or Computer Science

Tools

Linux cluster environment

Cloud environments

Job description

Post-Doctoral Research Associate - Computational Biologist

Job Title: Post-Doctoral Research Associate - Computational Biologist

Job Description Summary:

The Oakland University Institute for Data Science, in conjunction with scientists at the University of 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.

Minimum Qualifications:

Recent PhD in computational Biology, Statistics, Computer Science or related field. Deep understanding of next generation sequencing data and bioinformatics algorithms. Provide best practices data analysis, including QC and visualization, employing common NGS pipelines such as RNA‑seq, ChIP‑seq, ATAC‑seq, Methyl‑seq, DNA‑seq, and Microbiome Sequencing. Multi‑omics analysis is often needed. Proven experience working in a Linux cluster environment. Experience in cloud environments is also a plus. Familiarity with analysis of Biobank data, human microbiome sequence data (16S rRNA, metagenomics) and/or multi‑omics data (e.g. transcriptomics or metabolomic). Strong background in one or more of the following: pedigree analysis, machine learning, statistical methods, variant annotation and interpretation, Genome Wide Association Studies (GWAS), imaging analysis, and wearable sensor data analysis. Knowledge of general principles for regulatory compliance, including understanding of the NIH Security Best Practices for Controlled Access Data Subject to the NIH Genomic Data Sharing (GDS) Policy (aka dbGaP compliance). Experience with project management.

Required Documents