Work with Dr. Choonsik Lee
The Dosimetry Unit (DU) of the Radiation Epidemiology Branch (REB) in DCEG is seeking a postdoctoral fellow with training in computer science, artificial intelligence, and/or data science to develop and apply deep learning methods, including automated image segmentation and natural language processing, for radiation dosimetry and epidemiologic research.
DU investigators develop advanced methods and tools to estimate radiation dose and quantify uncertainty in large population studies, in collaboration with epidemiologists and statisticians. The successful candidate will receive interdisciplinary training in radiation dosimetry, radiation epidemiology, medical imaging, and biostatistics, and will be mentored toward increasing scientific independence during the fellowship. This fellowship is under the mentorship of Dr. Choonsik Lee.
Benefits
NCI offers highly competitive salaries and benefits, as well as opportunities for professional development. Learn more about the advantages of a DCEG fellowship
Qualifications
Applicants must hold or expect to soon attain a Ph.D. (or equivalent) in computer science, data science, artificial intelligence, medical physics, nuclear engineering, biomedical engineering, or a related field. Familiarity with Monte Carlo radiation transport simulation codes, computational human phantoms, and dose measurement techniques using physical phantoms is preferred.
Review of applications will continue until the position is filled.
To Apply
Applicants should send, via email, a cover letter, curriculum vitae, and the names of three referees to Dr. Choonsik Lee.
Applicants may be U.S. citizens, permanent residents, or foreign nationals (visa requirements apply).
Candidates are subject to a background investigation.
DHHS, NIH, and NCI are Equal Opportunity Employers
NIH provides reasonable accommodations to applicants with disabilities. If you require reasonable accommodation during any part of the application and hiring process, please notify us. The decision on granting reasonable accommodation will be made on a case-by-case basis.