Research Associate in Portland, Oregon at Oregon Health & Science University

Research Associate

US-OR-Portland

Requisition ID: 2026-41981
Position Category: Research
Job Type: Research
Position Type: Regular Full-Time
Posting Department: SM. Biomedical Engineering
Posting Salary Range: $53,081 - $88,587 per year, with offer based on experience, education and internal equity
Posting FTE: 1.00
Posting Schedule: Monday - Friday, occasional evening/weekends
Posting Hours: variable, typically 8:30am - 6:00pm
HR Mission: School of Medicine
Drug Testable: No

Department Overview

This position is a member of the Computational Biology Program at OHSU and will work in close collaboration with Dr. Yabing Chen's laboratory on computational studies of atherosclerosis and vascular disease.

The position will analyze and integrate single-cell RNA sequencing (scRNA-seq), spatial transcriptomics/spatial omics, bulk transcriptomic, epigenomic, imaging, and related biomedical datasets to define disease-associated cell states, vascular microenvironments, and molecular pathways involved in atherosclerosis. The employee will develop reproducible computational pipelines in R, Python, and Linux; perform quality control, cell-type/state annotation, differential and pathway analyses, spatial and multi-omics integration, and data visualization; and apply statistical, machine-learning, and computational biology methods to generate biologically testable hypotheses.

The position will work closely with computational scientists, vascular biologists, and experimental investigators in Dr. Yabing Chen's lab and collaborating groups to translate complex omics data into mechanistic insights, figures, manuscripts, and research proposals.

Function/Duties of Position

Single-Cell and Spatial Omics Analysis of Atherosclerosis

• Analyze scRNA-seq and spatial transcriptomics/spatial omics datasets from atherosclerosis and vascular-disease studies.
• Perform data quality control, normalization, dimensionality reduction, clustering, cell-type and cell-state annotation, differential expression, pathway analysis, trajectory/state-transition analysis, and spatially resolved characterization of vascular lesions and disease-associated cellular niches.

Multi-Omics Integration and Computational Analysis

• Integrate single-cell and spatial data with bulk RNA-seq, ATAC-seq, imaging, phenotypic, and other relevant datasets.
• Apply statistical, machine-learning, network, and multi-omics approaches to identify disease-associated regulatory programs, cell-cell interactions, molecular pathways, and candidate mechanisms relevant to atherosclerosis progression and vascular remodeling.

Reproducible Pipelines, Data Engineering, and Visualization

• Develop, optimize, document, and maintain reproducible analysis workflows using R, Python, Linux, Git, and appropriate cloud/container technologies.
• Build scalable data-processing pipelines, structured data systems, publication-quality visualizations, and reusable computational tools that support efficient analysis of high-dimensional genomic and spatial datasets.

Collaboration with Dr. Yabing Chen's Laboratory and Research Dissemination

• Work closely with Dr. Yabing Chen's laboratory and collaborating investigators to define computational questions, interpret results in the context of atherosclerosis biology, troubleshoot data and analysis issues, and translate findings into figures, presentations, manuscripts, grant applications, and follow-up experimental hypotheses.
• Communicate analytical methods and results clearly to both computational and experimental team members.

Other duties as assigned.

Required Qualifications

• Master's Degree in relevant field AND 3 years of relevant experience; OR

• Bachelor's Degree in relevant field AND 5 years of relevant experience.

Skills and Abilities

• Computational biology, bioinformatics, genomics, single-cell analysis, spatial omics, or related biomedical data science.

• Ability to work in interdisciplinary cardiovascular, vascular biology, atherosclerosis, or translational biomedical research teams.

• Required experience analyzing single-cell RNA-seq (scRNA-seq) and spatial transcriptomics/spatial omics data in atherosclerosis or closely related vascular/cardiovascular disease research.

• Proficiency in R and/or Python for bioinformatics and high-dimensional data analysis, preferably in Linux/Unix environments.

• Experience with scRNA-seq workflows including quality control, normalization, clustering, cell-type/state annotation, differential expression, pathway analysis, and data visualization.

• Experience with spatial-omics analysis, including integration of molecular measurements with tissue location and characterization of spatial cell states, neighborhoods, or disease-associated niches.

• Next-generation sequencing and/or multi-omics data such as bulk RNA-seq, ATAC-seq, genomic, epigenomic, imaging, or related biomedical datasets.

• Machine learning, deep learning, network analysis, image analysis, or computational modeling for biomedical applications.

• Proven proficiency developing scalable data pipelines or scientific software using Git, Docker, cloud platforms (AWS/GCP), SQL/NoSQL databases, REST APIs, or related data-engineering technologies.

• Experience generating publication-quality figures and translating computational results into biological hypotheses, manuscripts, presentations, or grant applications.

• Knowledge of vascular biology, vascular smooth muscle cells, immune-cell biology, inflammation, vascular calcification, or mechanisms of atherosclerosis.

• Understanding of basic statistics and ability to apply reproducible computational methods to biomedical research questions.

• Ability to manage multiple analyses, troubleshoot data and pipeline issues, communicate findings clearly, and work independently and collaboratively with computational and experimental investigators.

Preferred Qualifications

• Training or certification in cloud computing, data science, bioinformatics, or related computational technologies is preferred.

Additional Details

40 hours per week, may be sitting at a computer for extended periods of time.

Benefits

• Healthcare for full-time employees covered 100% and 88% for dependents.
• $50K of term life insurance provided at no cost to the employee.
• Two separate above market pension plans to choose from.
• Paid time off - 208 hours per year, prorated for part-time.
• Extended illness bank - 64 hours per year, prorated for part-time.
• 9 paid holidays per year.
• Substantial Tri-Met and C-Tran discounts.
• Employee Assistance Program.
• Childcare service discounts.
• Tuition reimbursement.
• Employee discounts to local and major businesses.