The Frederick National Laboratory is a Federally Funded Research and Development Center (FFRDC) sponsored by the National Cancer Institute (NCI) and operated by Leidos Biomedical Research, Inc. The lab addresses some of the most urgent and intractable problems in the biomedical sciences in cancer and AIDS, drug development and first-in-human clinical trials, applications of nanotechnology in medicine, and rapid response to emerging threats of infectious diseases.
Accountability, Compassion, Collaboration, Dedication, Integrity and Versatility; it's the FNL way.
The Cancer Genomics Research Laboratory (CGR) leverages state-of-the-art technologies to investigate the contribution of genetic variation, gene regulation and protein expression to cancer susceptibility and outcomes in support of the NCI's Division of Cancer Epidemiology and Genetics (DCEG), the world’s most comprehensive cancer epidemiology research group. CGR is located at the NCI-Shady Grove campus in Gaithersburg, MD and operated by Leidos Biomedical Research, Inc. as part of the Frederick National Laboratory for Cancer Research.
We care deeply about discovering the genetic and environmental determinants of cancer, and new approaches to cancer prevention, through our contributions to the molecular, genetic, and epidemiologic research of DCEG’s 70+ investigators and their collaborators throughout the world. We are looking to expand our sophisticated portfolio in the fields of multi-omic single-cell technologies, spatial transcriptomics and proteomics using established and emerging platforms. Within CGR, our Molecular and Digital Pathology Laboratory (MDPL) has expertise with tissues and histology which provides an invaluable resource for the development of image-based machine learning algorithms for different cancer types in diverse populations.
CGR has a large group of bioinformaticians with unique expertise, passion, and the opportunity to apply their skills to our rich and varied genotyping and sequencing datasets, generated in support of DCEG’s multidisciplinary family- and population-based studies. Working in concert with the epidemiologists, biostatisticians, and basic research scientists in DCEG’s intramural research program, CGR conducts genome-wide discovery studies and targeted regional approaches to identify the determinants of various forms of cancer.
The candidate will work closely with CGR bioinformaticians, DCEG investigators and MDPL scientists with a high degree of independence in the areas of single-cell and spatial biology. The role requires the candidate to:
- Work with a multi-disciplinary team to champion the development and analysis of reproducible, standardized workflows, in single-cell and spatial omics, by thoroughly researching the latest publications, developments and combining them with strong programming skills
- Explore, test, troubleshoot and benchmark novel and existing spatial software for analyzing datasets
- Stay atop the latest developments in the rapidly evolving area through attending lectures and forging collaborations Review, QC, and integrate single-cell and spatial datasets and perform downstream statistical analysis using phenotypic and clinical metadata
- Utilize statistical and machine-learning knowledge to implement spatial deconvolution and neighborhood analyses
- Organize results into clear presentations and concise summaries of work, useful for scientific interpretation
- Document all analyses and pipelines clearly and share with teams in support of FAIR research
- Utilize the collaborative mindset for scientific manuscript development, submission, revision activities with significant co-authorship opportunities
- Possession of PhD degree from an accredited college or university relevant to Bioinformatics, Computational Biology and Biostatistics. Foreign degrees must be evaluated for U.S. equivalency
- Minimum of two years of post-PhD experience in academia or industry with a strong publication record
- Prior experience with spatial omics data analysis and a fundamental understanding of analytical and statistical methods for spatial analysis and biomarker performance assessment
- Work with a high degree of independence and collaborate with internal and external researchers
- Strong programming skills (e.g., in R, Python) with experience in RStudio and Jupyter Notebooks
- Demonstrable shell scripting skills (e.g., bash, awk, sed)
- Experience working in a Linux environment (especially a HPC environment or cloud)
- Team player, organized, flexible with the ability to communicate with researchers from a variety of backgrounds
- Strong interest in development and validation of AI/deep learning models for image analysis
- Hands-on experience in processing of single-cell and spatial omics data utilizing latest bioinformatics tools such as Cell Ranger, Space Ranger, STARsolo, Seurat, Scanpy, Squidpy, Giotto, Cell2location etc.
- Experience working with data generated by a wide variety of genomic and proteomic platforms, including 10X genomics with short read or long read outputs, Visium and Xenium, Ultivue, Akoya, Nanostring GeoMX and CosMX
- Experience in developing and validating machine learning models for the analysis of spatial omics datasets Good understanding of algorithmic efficiency and working on high performance clusters for supporting large and diverse datasets
- Experience with various environment/dependency management tools (e.g. pip, venv, conda, renv)
To apply, please visit https://leidosbiomed.csod.com/ux/ats/careersite/4/home/requisition/3541?c=leidosbiomed