Location: Oak Ridge, TN
Cap-Exempt H-1B Position — No lottery required
Requisition Id 16393 **Overview:** As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation. **Key Responsibilities:** - Develop and validate Virtual Clinical Trial (VCT) frameworks to simulate patient populations and evaluate the efficacy of radiopharmaceutical interventions and imaging protocols. - Integrate AI/ML techniques with Monte Carlo simulations (e.g., Geant4, MCNP) to accelerate radiation transport calculations and image reconstruction workflows. - Optimize computational medicine codes for leadership-class HPC architectures to enable high-throughput population-scale simulations. - Collaborate across interdisciplinary teams spanning nuclear medicine, radiology, oncology, and computational physics. - Publish findings in high-impact journals and present at leading clinical and computational conferences. **Required Qualifications:** - Ph.D. (within 0–5 years) in Medical Physics, Nuclear Engineering, Biomedical Engineering, or a related computational field. - Strong programming skills in C++, Python, or similar languages relevant to scientific modeling and image processing. - Extensive experience with one or more of the following codes (or similar): Monte Carlo radiation transport codes (e.g., Geant4, GATE, TOPAS), DNA damage and repair modeling (MEDRAS), multicellular modes (Compucell, Physicell). - Experience working on High-Performance Computing (HPC) systems for large-scale data analysis or complex simulations. - Demonstrated abi