The Laboratory of Pathology, NCI (
), seeks applications from qualified computational post-doctoral candidates to fill a position in machine learning/cancer genomics related to the biological classification of cancer. Our group utilizes genome-wide methylation signatures for classification, leading to new discoveries in biological classification and new entities in cancer (PMID: 29539639). We are currently among the leaders in the U.S. with respect to the classification of tumors of the central nervous system (PMID: 34725697, 34555175, 34496929).
The proposed project will examine methylation-based classification more deeply, and also investigate whether and how the incorporation of complementary -omics data (e.g., RNAseq, chromatin accessibility, DNA copy number) can improve our understanding the biology and classification of these tumors. Additional opportunities include extending our classifier approach beyond brain tumors, utilizing a pan-cancer approach. A key advantage of this position is the potential for immediate impact into the clinic, as our activities in the Laboratory of Pathology translate basic research findings into clinical application for patients with cancer. An additional asset is access to an outstanding high-performance computing (HPC) cluster at the NIH (Biowulf,
The selected candidate will work as part of a multidisciplinary team of innovative scientists across the basic science-translation to examine how big epigenomic and genomic data analysis can aid in the discovery of tumor classes, biological relationships between cancer types and translation to patient benefit. Candidates should be self-motivated, driven, thorough and careful with the ability to multitask, think independently and work in a highly creative, interactive and fast-paced environment.
In addition to teamwork, they also will be expected to work independently as well-trained problem solvers. Effective communication and presentation skills are required. The selected candidate will keep accurate and complete records of all scientific experiments according to established procedures and ensure that these records and raw data are properly retained. They will draft manuscripts and patent applications and present work to internal and external collaborators as needed.Qualifications:
Applicants must have a doctoral degree in computer science, electrical engineering, statistics, biostatistics, bioinformatics, neuroscience, or a related area/experience with less than five years of postdoctoral research experience. Candidates should be proficient in bioinformatics, data analysis, algorithms, combinatorial optimization, machine learning and computational biology.
Candidates with a strong background and track record in big data analysis, especially in the context of cancer genomics as well as other multi-omics, will be preferentially considered. A new postdoc is expected to be highly motivated and productive, work independently as well as collaboratively with other lab members, and have strong work ethic and intellectual commitment to the cancer research. Proficient communication in both spoken and written English is strictly required, and excellent interpersonal and organizational skills are highly desired. Experience and facility with R and Python is required.
The salary will be commensurate with experience, based on the NIH Postdoctoral Intramural Research Training Award and Visiting Fellow scale; medical insurance coverage will be provided. The position is renewable for up to five years.To Apply:
Interested candidates should email a cover letter describing their research and career goals, a current curriculum vitae with a complete bibliography, and the names of and contact information for three references to
. Applicants also should indicate when they are available to start. Please note that the starting date of the fellowship is flexible, preferably within the first half of 2022.
The review of applications will begin immediately and will continue until the position is filled.
This position is subject to a background investigation. The NIH is dedicated to building a diverse community in its training and employment programs.