Postdoctoral Fellow

Employer
University of Texas MD Anderson Cancer Center
Location
Houston, Texas
Salary
Competitive
Closing date
Oct 13, 2022

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Discipline
Health Sciences
Organization Type
Healthcare/Hospital
A postdoctoral fellowship is available in the Department of Imaging Physics in the laboratory of S. Cheenu Kappadath, Ph.D. (https://www.mdanderson.org/kappadath-lab). Our research is focused on advancing quantitative imaging and dosimetry in theragnostic and clinical nuclear medicine (SPECT/CT and PET/CT) by applying physics, engineering, and mathematics to improve quantitative accuracy. Ongoing research projects include (1) Quantification of absorbed doses and dosimetry for 90Y-microsphere radioembolization therapies, (2) Image quantification for theragnostic and radionuclide therapies, (3) Optimization of SPECT/CT and PET/CT image generation, and (4) Quantification and response assessments with molecular breast images (MBI). Postdoctoral training will consist of conducting supervised and independent research and collaborating with a multi-disciplinary team comprising interventional radiologists, NM physicians, medical oncologists, and physicists.

LEARNING OBJECTIVES
The fellow will expand their knowledge and skills in nuclear medicine physics, image reconstruction, image quantification, data mining, and predictive modeling. They will also develop, train, and apply deep learning in large data environments. The fellow will participate and help to guide research and clinical collaborations and work with collaborators on funded projects and clinical trials. They will collaborate and coordinate with research and clinical stakeholders and communicate findings via abstracts, poster and podium presentations, and publications. The individual, under the supervision of a faculty mentor, will be prepared for an academic medical physicist career as an independent investigator.

ELIGIBILITY REQUIREMENTS
Applicants should have earned a Ph.D. in physics, computer sciences, engineering, or related fields. Experience with radioembolization, Monte Carlo simulations, machine learning, statistical modeling, and analysis is desired.

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