Research Associate lll, Computational Pathoepidemiology, MDPL, CGR
Job ID: req3726
Employee Type: exempt full-time
Division: Clinical Research Program
Facility: Rockville: 9615 MedCtrDr
Location: 9615 Medical Center Drive, Rockville, MD 20850 USA
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) investigates the contribution of germline and somatic genetic variation 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 Rockville, MD and operated by Leidos Biomedical Research, Inc. 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 the 70+ principal investigators (PIs) in DCEG. Working in concert with epidemiologists, biostatisticians and basic research scientists in DCEG’s intramural research program, CGR provides the capacity to conduct genome-wide discovery studies and targeted regional approaches to identify the heritable determinants of various forms of cancer. Within CGR, the Molecular and Digital Pathology Laboratory (MDPL) is dedicated to the support of genetic and epidemiologic studies for DCEG investigators through tissue profiling and image analysis. This includes the design and analysis of high throughput molecular epidemiologic studies using various types of “omics” technologies such as AI-based histomics, proteomics (immunohistochemistry, multiplex immunofluorescence, digital spatial profiling), and somatic genomic analyses. We are seeking a computational Research Associate to join our team and support large-scale, international epidemiological research on cancer etiology and progression through integrative analyses of risk factors and tissue profiling to inform prevention and clinical strategies.
CGR is recruiting for a Research Associate position to be part of a group focused on large-scale image analysis in support of researchers within DCEG’s Integrative Tumor Epidemiology Branch (ITEB). The position will involve working closely with an ITEB PI and MDPL staff to define priorities, monitor study progress, manage project-specific imaging data, run image analysis, and manage the resulting data. More specifically, key responsibilities for this position include:
- Coordinate with existing laboratory, project management and analytical staff to track study progress from conception (developing analytical plans and study protocols) through completion (preparation of summary reports for presentation and publication).
- Execute statistical analysis of epidemiologic and tissue-based data derived from image analysis of whole slide and tissue microarray (TMA) images of hematoxylin and eosin (H&E), immunohistochemical (IHC), immunofluorescence (IF), and other state-of-the-art staining modalities including emerging spatial technologies.
- Accurately maintain thorough documentation and version control of data management activities (data cleaning, harmonization, and statistical analysis)
- Support process improvement and assay development activities in collaboration with CGR development resources and senior laboratory management staff.
- Participate in the writing and review of laboratory SOPs to help ensure image quality and data is suitable for downstream analyses. Follow established protocols and assist with deviations and corrective actions.
- Meet expected schedules and performance expectations in a fast-paced research laboratory environment.
- Support a culture of continuous improvement and accountability; contribute to troubleshooting of issues associated with assay processing.
- Support process improvement and assay development activities in collaboration with CGR development resources and senior laboratory management staff
To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below:
- Possession of a bachelor’s degree from an accredited college or university according to the Council for Higher Education Accreditation in a field related to epidemiology or biomedical research. Foreign degrees must be evaluated for U.S. equivalency.
- A minimum of 8 (eight) years related experience in the fields of histopathology, digital pathology and/or data science
- Strong analytical skills and experience with statistical software applications used in public health research, such as Stata and/or R, Python, C++ (please indicate relevant software applications and degree of proficiency)
- Strong understanding of molecular biology
- Careful attention to detail
- Critical thinking skills and ability to identify, document and analyze important variables in an experiment
- Demonstrated ability to learn and adopt new concepts in biomarker analysis
- Collaborative, team-oriented approach to solving problems and achieving goals
- Strong organization skills, ability to multitask and manage time effectively
- Excellent communication skills (verbal and written)
- Working knowledge of Windows-based computer operating systems and Microsoft Office suite
- Must be able to obtain and maintain a security clearance
Candidates with these desired skills will be given preferential consideration:
- Master’s degree in epidemiology, public health, data science, biostatistics, or a related field
- Familiarity with eLN and/or LIMS systems for sample and workflow tracking
- Knowledge of basic histological techniques including processing and embedding, microtomy/cryotomy, H&E staining, slide coverslipping and how these variables relate to image and data quality
- Experience with digital slide scanners for brightfield and fluorescent applications
- Microscopic recognition of major human tissue types.
- A general understanding of database organization and structure.
- Experience with image analysis software such as Halo, QuPath, Visiopharm, Definiens, etc. (please indicate relevant software applications and level of proficiency)
- Experience in the application of artificial intelligence (machine learning or deep learning) to pathology image analysis
Commitment to Diversity
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, 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.