Postdoctoral Researcher

Overview

We are seeking a highly motivated Postdoctoral Researcher to join our interdisciplinary team

developing personalized, time-based voxel-wise risk models of brain metastases (BM)

formation. This NIH-funded project integrates medical imaging, deep learning, and

quantitative modeling to improve patient-specific prediction of BM occurrence and support

novel radiation treatment planning approaches.

The postdoc will have the opportunity to work at the intersection of medical physics,

computational imaging, oncology, and AI, contributing to a project with direct translational

relevance for improving outcomes in patients with non-small cell lung cancer (NSCLC) and brain

metastases.

Key Responsibilities

Model Development:

• Design and implement Bayesian deep learning architectures (e.g., Bayesian UNET 3D within MONAI/PyTorch) to integrate multimodal imaging data (anatomy, perfusion, functional tracks) with population BM risk maps.
• Develop time-based prediction models incorporating lesion temporal history for longitudinal risk estimation at 3, 6, and 12 months.

Data Integration & Preprocessing:

• Work with a multi-institutional NSCLC BM imaging database (https://www.nature.com/articles/s41467-025-59584-7) including MRI (anatomical and perfusion), BM contours, and clinical annotations.
• Perform advanced image registration (rigid deformable) to map lesions and perfusion data onto MNI brain space and transform back into patient space.
• Automate anatomical and functional brain segmentation (e.g., FastSurfer, HCP atlas integration).

Model Evaluation:

• Quantitatively assess prediction accuracy, uncertainty, and robustness using cross-validation and longitudinal validation cohorts.o Employ advanced hyperparameter optimization strategies (e.g., Optuna) and Bayesian inference methods to enhance reliability.

Collaboration & Dissemination:

• Work closely with physicists, oncologists, radiologists, and biostatisticians across multiple institutions.
• Publish high-impact scientific articles and present at major conferences (AAPM, ASTRO, MICCAI, IBSI).
• Contribute to mentoring graduate students and residents in imaging and AI research.
• Participation to 1-2 conference per year for development, networking and learning opportunity.

Qualifications

Required:

• PhD in Medical Physics, Biomedical Engineering, Computer Science, Applied
• Mathematics, or related field.
• Strong background in medical image analysis, machine learning/deep learning, or computational modeling.
• Proficiency in Python and deep learning frameworks (PyTorch, MONAI, TensorFlow).
• Experience with neuroimaging (MRI registration, segmentation, perfusion analysis) and 3D data handling.

Preferred:

• Prior research in oncology, radiation therapy, or neuroimaging applications.
• Experience with Bayesian modeling, uncertainty quantification, and survival analysis.
• Familiarity with multi-institutional datasets, data harmonization, and reproducible workflows.
• Strong publication record and evidence of independent research capability.

Opportunities & Career Development

• Work on a seminal NIH-funded project with a unique multi-institutional dataset.
• Access to cutting-edge computational infrastructure and clinical imaging resources.
• Mentorship and career development support from an interdisciplinary team of investigators.
• Potential to transition into independent funding opportunities (e.g., NIH K99/R00, early faculty positions).

Application Instructions

Applicants should submit:

• A cover letter detailing research experience, interests, and career goals.
• Curriculum vitae (CV).
• Contact information for 2-3 references.

Send applications or inquiries to:

Olivier Morin, PhD

Principal Investigator

Olivier.Morin@ucsf.edu