Research Associate

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.

• 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.

• 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.

• 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.

• 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.

• 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.

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

• 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.

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

 

We are Oregon's only public academic health center. In addition to caring for patients, we lead groundbreaking research. We also train the next generation of health care professionals. As Portland's largest employer, we give you opportunities to learn and advance in a system of hospitals and clinics across Oregon and Southwest Washington. All are welcome. OHSU welcomes people of all ages, ethnicities, genders, national origins, religions and sexual orientations. We are striving to build an anti-racist, multicultural institution and encourage people with diverse backgrounds to apply. To request reasonable accommodation, contact askhr@ohsu.edu