Job Description
Job ID: req4512 Employee Type: exempt full-time Division: Clinical Research Program Facility: Rockville: 9609 MedCtrDr Location: USA The Frederick National Laboratory is 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. Program Description We are seeking a skilled and motivated Computational Scientist II to join the Cancer Genomics Research Laboratory (CGR), located at the National Cancer Institute (NCI) Shady Grove campus in Rockville, MD (actual work location negotiable) . CGR is operated by Leidos Biomedical Research, Inc., and collaborates with the NCI’s Division of Cancer Epidemiology and Genetics (DCEG) - the world’s leading cancer epidemiology research group. Our scientific team leverages cutting-edge technologies to investigate genetic, epigenetic, transcriptomic, proteomic, and molecular factors that drive cancer susceptibility and outcomes. We are deeply committed to the mission of discovering the causes of cancer and advancing new prevention strategies through our contributions to DCEG’s pioneering research. Our team of CGR bioinformaticians supports DCEG’s multidisciplinary family- and population-based studies by working closely with epidemiologists, biostatisticians, and basic research scientists in DCEG’s intramural research program. We provide end-to-end bioinformatics support for genome-wide association studies (GWAS), methylation profiling, targeted, whole-exome, whole-transcriptome and whole-genome sequencing along with viral and metagenomic studies from both short- and long-read sequencing platforms. Our work spans germline and somatic variant detection, structural and copy number variation, microsatellite analysis, mutational signature profiling, gene and isoform expression, base modification analysis, viral and bacterial genomics, and more. Additionally, we advance cancer research by integrating latest technologies such as single-cell and spatial transcriptomics, multiomics and proteomics, in collaboration with the Functional and Molecular and Digital Pathology Laboratory groups within CGR. We extensively analyze large population databases such as All of Us, UK Biobank, gnomAD and 1000 Genomes to inform and validate GWAS signals, study the association between genetic variation and gene expression, protein levels, and metabolites and to develop polygenic risk scores across multiple populations. Our bioinformatics team develops and implements sophisticated, cloud-enabled pipelines and data analysis methodologies, blending traditional bioinformatics and statistical approaches with cutting-edge techniques like machine learning, deep learning, and generative AI. We prioritize reproducibility through containerization, workflow management tools, thorough benchmarking, and detailed workflow documentation. Our infrastructure and data management team works closely with researchers and bioinformaticians to maintain and optimize a high-performance computing (HPC) cluster, provision cloud environments, and curate and share large datasets. The successful candidate will provide dedicated scientific and analytical support to the Integrative Tumor Epidemiology Branch (ITEB) through their expertise in tumor genomics, lung cancer biology, and epidemiology. They will advance the Sherlock-Lung Study, a large-scale initiative investigating the genomic, transcriptomic, and methylation landscapes of lung cancer in never smokers, as well as their spatial architecture, to uncover mutational processes, molecular changes, and tumor evolution. The Computational Scientist II will lead integrative analyses and scientific interpretation of somatic high-coverage whole-genome sequencing (WGS) and multi-omics datasets from the Sherlock-Lung cohort, consisting of over 3,000 subjects. This position centers on hypothesis-driven investigation that combines biological and computational expertise, with leadership in producing high-impact publications that advance understanding of lung cancer development and progression. Key Roles/Responsibilities Formulate and test biological hypotheses related to mutational processes, intra-tumor heterogeneity, clonal architecture, and evolutionary dynamics in lung cancer. Lead integrative analyses of somatic and germline variation (SNVs, indels, structural variants, copy number alterations), mutational signatures, and driver events using large-scale short-read and long-read WGS and multi-omics datasets. Apply advanced statistical approaches to ex