Frederick National Laboratory for Cancer Research
Bioinformatics Analyst III - SC and Spatial, CGR
Frederick National Laboratory for Cancer Research, Rockville, Maryland, us, 20849
Bioinformatics Analyst III - SC and Spatial, CGR
Join to apply for the Bioinformatics Analyst III - SC and Spatial, CGR role at Frederick National Laboratory for Cancer Research.
Overview The Frederick National Laboratory is operated by Leidos Biomedical Research, Inc. The CGR team studies germline and somatic factors that drive cancer susceptibility and outcomes, integrating single-cell, multiomics, spatial transcriptomics, and proteomics in collaboration with DCEG and other CGR groups. The analyst provides end-to-end bioinformatics support for genome-wide association studies, methylation, targeted sequencing, whole-exome, whole-transcriptome, and whole-genome sequencing, including viral and metagenomic analyses. The role emphasizes independence, collaboration, and the application of advanced statistical, machine learning, and deep learning methods to large-scale datasets.
Key Roles/Responsibilities
Provide expert support for single cell, multiomic, and spatial omics projects by collaborating closely with DCEG investigators, CGR bioinformaticians, and CGR laboratory scientists.
Implement state-of-the-art computational approaches to support standardized and reproducible analyses for spatial multi-modal omics.
Research and benchmark existing and emerging software tools and integrate multi-modal omics data to advance large-scale oncology research.
Stay up to date with advancements in the field through literature review, seminars, and cross-disciplinary collaborations.
Perform quality control, batch correction, data integration, cell typing, and downstream analysis of single-cell, multiomic, and spatial transcriptomics datasets, incorporating relevant phenotypic and clinical metadata.
Apply statistical, machine learning, and deep learning methods for cell segmentation, annotation, and advanced modeling.
Present analytical results through clear, interpretable visualizations and reports to support scientific insights and communication.
Maintain detailed documentation of analyses, workflows, and pipelines to ensure adherence to FAIR data principles. Position requires routine use of GitHub, JIRA, markdown for documentation.
Contribute to manuscript writing, submission, and revisions, with strong opportunities for co-authorship.
Basic Qualifications
Bachelor’s degree in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related field from an accredited institution. Foreign degrees must be evaluated for U.S. equivalency.
At least five (5) years of directly related analytical or bioinformatics pipeline development experience.
Demonstrated experience with single cell, single-nuclear multiomic, and spatial omics data analysis, including biomarker discovery and spatial profiling methods.
Ability to work independently and collaboratively with internal and external investigators.
Strong programming skills in R and/or Python using tools like RStudio and Jupyter Notebooks.
Hands-on experience with tools such as Cell Ranger, Space Ranger, Stardist, Harmony, RCTD, Seurat, SingleR, Scanpy, Squidpy, and Bioconductor packages.
Proficient in creating high-quality visualizations using ggplot2 (R) and Python visualization libraries (Matplotlib, Seaborn, Plotly).
Experience with DevOps tools such as Docker/Singularity, and GitHub for code management and reproducible research.
Proficiency in shell scripting (e.g., Bash, AWK, SED).
Experience working in Linux-based HPC or cloud environments.
Strong communication skills and ability to collaborate across diverse research backgrounds.
Ability to obtain and maintain a security clearance.
Preferred Qualifications
Master's or PhD in bioinformatics, computer science, computational biology or related field.
Experience analyzing data from diverse single cell and spatial applications, including 10X Chromium, Visium/Xenium platforms, Bruker/Nanostring GeoMX/CosMX, and other sequencing platforms (Illumina, PacBio, ONT).
End-to-end processing and analysis experience of single-cell RNA-Seq, single-cell ATAC-Seq, single-nuclear multiomic, and spatial transcriptomic datasets.
Experience developing, validating, and applying AI and deep learning models for imaging, spatial and multiomic applications.
Ability to optimize algorithms for HPC systems and parallelize large dataset processing.
Proficiency with dependency/environment management tools (e.g., pip, conda, renv, venv).
Additional experience analyzing bulk transcriptomics, proteomics, metabolomics, or cancer genomics data from NGS platforms is a plus.
Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, color, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
Pay and Benefits Pay and benefits are fundamental to any career decision. The posted pay range for this job is 109,600.00 - 188,250.00 full-time equivalent. Details on compensation and benefits are provided by the employer; actual offers depend on responsibilities, education, experience, and skills. This salary range will vary with scheduled hours for part-time positions.
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Research, Analyst, and Information Technology
#J-18808-Ljbffr
Overview The Frederick National Laboratory is operated by Leidos Biomedical Research, Inc. The CGR team studies germline and somatic factors that drive cancer susceptibility and outcomes, integrating single-cell, multiomics, spatial transcriptomics, and proteomics in collaboration with DCEG and other CGR groups. The analyst provides end-to-end bioinformatics support for genome-wide association studies, methylation, targeted sequencing, whole-exome, whole-transcriptome, and whole-genome sequencing, including viral and metagenomic analyses. The role emphasizes independence, collaboration, and the application of advanced statistical, machine learning, and deep learning methods to large-scale datasets.
Key Roles/Responsibilities
Provide expert support for single cell, multiomic, and spatial omics projects by collaborating closely with DCEG investigators, CGR bioinformaticians, and CGR laboratory scientists.
Implement state-of-the-art computational approaches to support standardized and reproducible analyses for spatial multi-modal omics.
Research and benchmark existing and emerging software tools and integrate multi-modal omics data to advance large-scale oncology research.
Stay up to date with advancements in the field through literature review, seminars, and cross-disciplinary collaborations.
Perform quality control, batch correction, data integration, cell typing, and downstream analysis of single-cell, multiomic, and spatial transcriptomics datasets, incorporating relevant phenotypic and clinical metadata.
Apply statistical, machine learning, and deep learning methods for cell segmentation, annotation, and advanced modeling.
Present analytical results through clear, interpretable visualizations and reports to support scientific insights and communication.
Maintain detailed documentation of analyses, workflows, and pipelines to ensure adherence to FAIR data principles. Position requires routine use of GitHub, JIRA, markdown for documentation.
Contribute to manuscript writing, submission, and revisions, with strong opportunities for co-authorship.
Basic Qualifications
Bachelor’s degree in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related field from an accredited institution. Foreign degrees must be evaluated for U.S. equivalency.
At least five (5) years of directly related analytical or bioinformatics pipeline development experience.
Demonstrated experience with single cell, single-nuclear multiomic, and spatial omics data analysis, including biomarker discovery and spatial profiling methods.
Ability to work independently and collaboratively with internal and external investigators.
Strong programming skills in R and/or Python using tools like RStudio and Jupyter Notebooks.
Hands-on experience with tools such as Cell Ranger, Space Ranger, Stardist, Harmony, RCTD, Seurat, SingleR, Scanpy, Squidpy, and Bioconductor packages.
Proficient in creating high-quality visualizations using ggplot2 (R) and Python visualization libraries (Matplotlib, Seaborn, Plotly).
Experience with DevOps tools such as Docker/Singularity, and GitHub for code management and reproducible research.
Proficiency in shell scripting (e.g., Bash, AWK, SED).
Experience working in Linux-based HPC or cloud environments.
Strong communication skills and ability to collaborate across diverse research backgrounds.
Ability to obtain and maintain a security clearance.
Preferred Qualifications
Master's or PhD in bioinformatics, computer science, computational biology or related field.
Experience analyzing data from diverse single cell and spatial applications, including 10X Chromium, Visium/Xenium platforms, Bruker/Nanostring GeoMX/CosMX, and other sequencing platforms (Illumina, PacBio, ONT).
End-to-end processing and analysis experience of single-cell RNA-Seq, single-cell ATAC-Seq, single-nuclear multiomic, and spatial transcriptomic datasets.
Experience developing, validating, and applying AI and deep learning models for imaging, spatial and multiomic applications.
Ability to optimize algorithms for HPC systems and parallelize large dataset processing.
Proficiency with dependency/environment management tools (e.g., pip, conda, renv, venv).
Additional experience analyzing bulk transcriptomics, proteomics, metabolomics, or cancer genomics data from NGS platforms is a plus.
Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, color, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.
Pay and Benefits Pay and benefits are fundamental to any career decision. The posted pay range for this job is 109,600.00 - 188,250.00 full-time equivalent. Details on compensation and benefits are provided by the employer; actual offers depend on responsibilities, education, experience, and skills. This salary range will vary with scheduled hours for part-time positions.
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Research, Analyst, and Information Technology
#J-18808-Ljbffr