Ph. D. position openings
Ph. D. position openings
(application due date: Dec./1/2025, proposed start date: Sept./01/2026)
(application due date: Dec./1/2025, proposed start date: Sept./01/2026)
In this project, you will work on computational modeling methods, including but not limited to Monte Carlo methods, Adjoint Monte Carlo methods, and Deterministic approaches.
The aims of this project are to (1) develop a resource-efficient framework for modeling medical imaging devices (Photon-counting CT, Electronic Portal Imaging Devices, Digital Breast Tomosynthesis) and treatment modalities (Clinical Linear Accelerator and Proton treatment machine) and (2) explore clinical applications of computational modeling approaches, such as in-silico clinical trials or radiotherapy optimization.
Candidates should have a background in physics, nuclear physics, medical physics, or a related field.
Preference will be given to candidates with experience in computational modeling.
Lee, H., Shin, J., Verbug, J., Bobić, M., Winey, B., Schuemann, J., Paganetti, H., “MOQUI: An open-source GPU-based Monte Carlo code for proton dose calculation with efficient data structure,” Physics in Medicine and Biology, 67(17) (2022)
Lee, H., Cheon, B.-W., Feld, J., Grogg, K., Perl, J., Faddegon, B., Min, C. H., Paganetti, H., Schuemann, J., “TOPAS-imaging: An extension to the TOPAS simulation toolkit for medical imaging systems,” Physics in Medicine and Biology, 68(8), 084001 (2023)
Lee, H., "Monte Carlo methods for medical imaging research," Biomedical Engineering Letter, 14, 1195-1205 (2024)
Meyer, I., Peters, N., Tamborino, G., Lee, H., Bertolet, A., Faddegon, B. A., Mille, M. M., Lee, C., Schuemann, J., Paganetti, H. "A framework for in-field and out-of-field patient specific secondary cancer risk estimates from treatment plans using the TOPAS Monte Carlo system," Physics in Medicine and Biology, 69(16), 165023 (2024)
In this project, you will work on AI models for early cancer detection and prognosis prediction after cancer treatment.
The aims of this project are (1) to develop a human-interpretable AI model for cancer detection and (2) to develop a multi-modal AI for predicting the clinical outcome of radiotherapy.
Candidates should have a background in physics, nuclear physics, medical physics, computer science, or a related field.
Preference will be given to candidates with experience in machine learning or image processing.
Chamseddine, I., Shah, K., Lee, H., Ehret, F., Schuemann, J., Bertolet, A., Shih, H. A., Paganetti, H., “Decoding Patient Heterogeneity Influencing Radiation-Induced Brain Necrosis,” Clinical Cancer Research (2024)
Shah., K. D., Yeap, B. Y., Lee, H., Soetan, Z. O., Moteabbed, M., Muise, S., Cowan, J., Remillard, K., Silvia, B., Mendenhall, N. P., Soffen, E., Mishra, M. V., Kamran, S. C., Miyamoto, D. T., Paganetti, H., Efstathiou, J. A., Chamseddine, I., “Predictive Model of Acute Rectal Toxicity in Prostate Cancer Treated With Radiotherapy,” JCO Clinical Cancer Informatics, 9 (2025)
Please indicate which project you are interested in.