Postdoctoral Fellow in Medical Image Segmentation and Multiphysics Modeling for MR-Guided Brain Hyperthermia - Radiation Oncology

Position Summary The Department of Radiation Oncology at the University of Maryland School of Medicine (UMSOM) is seeking a highly motivated postdoctoral fellow for a full-time, three-year appointment. The fellow will contribute to an NIH-funded project developing a non-invasive, MR-compatible hyperthermia system for brain tumor treatment. The fellow’s primary focus will be the development of high-resolution virtual anatomical models of the human head for patient-specific electromagnetic and thermal simulations. This work will include detailed tissue segmentation from medical images, integration of automatic and semi-automatic segmentation algorithms, refinement of anatomical geometries, and optimization of surface and volumetric meshes. The fellow will also develop and evaluate multiphysics computational models coupling microwave electromagnetic energy deposition with bioheat transfer to predict specific absorption rate and temperature distributions in the brain and surrounding tissues. The fellow will also have opportunities to shadow clinical hyperthermia treatments across deep, superficial, and interstitial modalities, providing direct exposure to quality assurance, treatment planning, treatment delivery, thermometry, and clinical workflow. Primary Responsibilities * Develop anatomically detailed, high-resolution virtual models of the human head from CT and MRI datasets. * 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 byDr. 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 theHyperthermia Therapy Practice School; participation may be available subject to eligibility and departmental approval. 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 withSynopsys Simpleware, ZMT iSeg, or comparable softwarefor image segmentation, anatomical-model generation, and preparation of simulation-ready models. * Experience withFEM/FDTD spatial discretization, geometry or voxel-model preparation, material-property assignment, and mesh/grid-quality assessment. * Experience withSim4Life, COMSOL, Ansys, or comparable simulation software. * Knowledge ofRF/MW propagation and bioheat-transfer modeling. * Experienceautomating image-processing or simulation workflowsusing Python, MATLAB, C , or similar programming languages. * Experience withDICOM data, medical-image registration, high-performance computing, or GPU-based computation. * Familiarity withmachine-learning or deep-learning methodsfor medical-image segmentation. * Previous experience inthermal therapies for cancer treatment(hyperthermia or thermal ablation), particularly in treatment planning and/or medical-device development. Job: *Postdoctoral Fellows Organization: *School of Medicine - Clinical Departments Title: Postdoctoral Fellow in Medical Image Segmentation and Multiphysics Modeling for MR-Guided Brain Hyperthermia - Radiation Oncology Location: null Requisition ID: 260000UP