Perform manual, semi-automatic, and automatic segmentation of tissues relevant to electromagnetic and thermal modeling.

Integrate and evaluate automatic segmentation algorithms within a reproducible anatomical-modeling workflow.

Refine segmented geometries and optimize surface and volumetric mesh generation for anatomical accuracy, numerical stability, and computational efficiency.

Develop multiphysics models coupling electromagnetic field simulations, microwave power deposition, tissue perfusion, and bioheat transfer.

Perform mesh-convergence, sensitivity, uncertainty, and model-verification analyses.

Compare computational predictions with phantom, preclinical, and other experimental measurements.

Analyze simulation results and prepare manuscripts, conference presentations, and technical reports.

Collaborate with medical physicists, engineers, neurosurgeons, imaging scientists, and industry partners.

Shadow clinical hyperthermia treatments—including treatment planning, patient setup, thermal monitoring, and quality assurance—to understand the clinical constraints and translational requirements that should inform model development. This experience will be observational and educational rather than an independent clinical role.

Mentorship and Training

The fellow will be primarily supervised by Dr. Dario Rodrigues, Associate Professor and Lead Hyperthermia Physicist in the Department of Radiation Oncology. Dr. Rodrigues brings 18 years of research experience in computational modeling, applicator design, and experimental validation, together with more than 12 years of clinical hyperthermia experience and leadership service in major international thermal therapy societies. This combination of computational, experimental, and clinical expertise will provide the fellow with an integrated training environment in which model development is closely connected to technical feasibility and clinical translation. Close proximity to Dr. Rodrigues's office will facilitate frequent informal interactions, supplemented by at least one formal meeting each week and regular multidisciplinary project meetings.

The fellow will receive training in computational medical imaging, patient-specific multiphysics modeling, thermal therapy, translational research, scientific communication, and manuscript and grant preparation. The fellow will be encouraged and supported in presenting findings at major national and international conferences and publishing in peer-reviewed journals. The Department also offers multiple CME-accredited training opportunities, including the Hyperthermia Therapy Practice School ; participation may be available subject to eligibility and departmental approval.

Qualifications :

Required Qualifications

• PhD in medical physics, biomedical engineering, electrical engineering, mechanical engineering, computer science, applied physics, applied mathematics, or a closely related field.
• At least one year of direct research experience in medical-image segmentation and three-dimensional anatomical-model development.
• Experience processing CT, MRI, or comparable volumetric medical-imaging datasets.
• Demonstrated ability to conduct independent quantitative research and analyze complex computational results.
• Strong scientific writing, communication, and organizational skills.
• A record of peer-reviewed publications or other evidence of research productivity.

Preferred Qualifications

• Experience with Synopsys Simpleware or comparable software for image segmentation, anatomical-model generation, and mesh creation.
• Experience with COMSOL Multiphysics, Ansys, or comparable finite-element or multiphysics simulation software .
• Knowledge of electromagnetic modeling, radiofrequency or microwave propagation, heat transfer, or bioheat-transfer modeling.
• Experience with finite-element meshing, geometry cleanup, material-property assignment, and mesh-quality assessment.
• Experience automating image-processing or simulation workflows using Python, MATLAB, C++, or similar programming languages.
• Familiarity with machine-learning or deep-learning methods for medical-image segmentation.
• Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation.
• Previous experience in medical physics, thermal therapy, hyperthermia, treatment planning, or medical-device development.

Perform manual, semi-automatic, and automatic segmentation of tissues relevant to electromagnetic and thermal modeling.

Integrate and evaluate automatic segmentation algorithms within a reproducible anatomical-modeling workflow.

Refine segmented geometries and optimize surface and volumetric mesh generation for anatomical accuracy, numerical stability, and computational efficiency.

Develop multiphysics models coupling electromagnetic field simulations, microwave power deposition, tissue perfusion, and bioheat transfer.

Perform mesh-convergence, sensitivity, uncertainty, and model-verification analyses.

Compare computational predictions with phantom, preclinical, and other experimental measurements.

Analyze simulation results and prepare manuscripts, conference presentations, and technical reports.

Collaborate with medical physicists, engineers, neurosurgeons, imaging scientists, and industry partners.

Shadow clinical hyperthermia treatments—including treatment planning, patient setup, thermal monitoring, and quality assurance—to understand the clinical constraints and translational requirements that should inform model development. This experience will be observational and educational rather than an independent clinical role.

Mentorship and Training

The fellow will be primarily supervised by Dr. Dario Rodrigues, Associate Professor and Lead Hyperthermia Physicist in the Department of Radiation Oncology. Dr. Rodrigues brings 18 years of research experience in computational modeling, applicator design, and experimental validation, together with more than 12 years of clinical hyperthermia experience and leadership service in major international thermal therapy societies. This combination of computational, experimental, and clinical expertise will provide the fellow with an integrated training environment in which model development is closely connected to technical feasibility and clinical translation. Close proximity to Dr. Rodrigues's office will facilitate frequent informal interactions, supplemented by at least one formal meeting each week and regular multidisciplinary project meetings.

The fellow will receive training in computational medical imaging, patient-specific multiphysics modeling, thermal therapy, translational research, scientific communication, and manuscript and grant preparation. The fellow will be encouraged and supported in presenting findings at major national and international conferences and publishing in peer-reviewed journals. The Department also offers multiple CME-accredited training opportunities, including the Hyperthermia Therapy Practice School ; participation may be available subject to eligibility and departmental approval.

Qualifications :

Required Qualifications

• PhD in medical physics, biomedical engineering, electrical engineering, mechanical engineering, computer science, applied physics, applied mathematics, or a closely related field.
• At least one year of direct research experience in medical-image segmentation and three-dimensional anatomical-model development.
• Experience processing CT, MRI, or comparable volumetric medical-imaging datasets.
• Demonstrated ability to conduct independent quantitative research and analyze complex computational results.
• Strong scientific writing, communication, and organizational skills.
• A record of peer-reviewed publications or other evidence of research productivity.

Preferred Qualifications

• Experience with Synopsys Simpleware or comparable software for image segmentation, anatomical-model generation, and mesh creation.
• Experience with COMSOL Multiphysics, Ansys, or comparable finite-element or multiphysics simulation software .
• Knowledge of electromagnetic modeling, radiofrequency or microwave propagation, heat transfer, or bioheat-transfer modeling.
• Experience with finite-element meshing, geometry cleanup, material-property assignment, and mesh-quality assessment.
• Experience automating image-processing or simulation workflows using Python, MATLAB, C++, or similar programming languages.
• Familiarity with machine-learning or deep-learning methods for medical-image segmentation.
• Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation.
• Previous experience in medical physics, thermal therapy, hyperthermia, treatment planning, or medical-device development.