Key Research Areas (Candidates may focus on one or bridge multiple domains):

• Neuroimaging & Predictive Modeling: Develop advanced statistical and ML methods for large-scale neuroimaging to predict brain age, model disease trajectories, and discover neurological biomarkers.
• Functional Network Modeling & Deep Learning: Apply novel DL architectures and generative models to decode dynamic functional connectivity and brain network organization.
• CT Analysis & Urological Applications: Utilize computer vision and radiomics for automated segmentation, classification, treatment response prediction, and robust multi-site modeling in urological health.

Qualifications

Education: Ph.D. in Computer Science, Biomedical Engineering, Applied Mathematics, Neuroscience, or a related quantitative field.

Core Expertise: Strong background in machine learning, deep learning, computer vision, and medical image analysis.

Agentic AI Focus: A strong interest in, or willingness to learn and develop, Agentic AI applications to enhance medical imaging workflows and research.

Track Record: Proven ability to conduct independent research, collaborate effectively, and publish in top-tier venues.

Application Instructions

Required documents for upload: Curriculum Vitae (CV), Research Statement (Highlighting your preferred research areas and how your expertise aligns with or bridges them), and References: Names and contact information for three professional references (requests will be sent directly through Interfolio)

Key Research Areas (Candidates may focus on one or bridge multiple domains):

• Neuroimaging & Predictive Modeling: Develop advanced statistical and ML methods for large-scale neuroimaging to predict brain age, model disease trajectories, and discover neurological biomarkers.
• Functional Network Modeling & Deep Learning: Apply novel DL architectures and generative models to decode dynamic functional connectivity and brain network organization.
• CT Analysis & Urological Applications: Utilize computer vision and radiomics for automated segmentation, classification, treatment response prediction, and robust multi-site modeling in urological health.

Qualifications

Education: Ph.D. in Computer Science, Biomedical Engineering, Applied Mathematics, Neuroscience, or a related quantitative field.

Core Expertise: Strong background in machine learning, deep learning, computer vision, and medical image analysis.

Agentic AI Focus: A strong interest in, or willingness to learn and develop, Agentic AI applications to enhance medical imaging workflows and research.

Track Record: Proven ability to conduct independent research, collaborate effectively, and publish in top-tier venues.

Application Instructions

Required documents for upload: Curriculum Vitae (CV), Research Statement (Highlighting your preferred research areas and how your expertise aligns with or bridges them), and References: Names and contact information for three professional references (requests will be sent directly through Interfolio)