Open Postdoctoral position, faculty mentor Tamar Green

Position: Postdoctoral Research in Neuroimaging 

The Bridge Lab, led by Dr. Tamar Green at Stanford University, investigates how genetic alterations shape brain development and neuropsychiatric outcomes, with the goal of improving mental health and developmental trajectories in children. Our work emphasizes mechanism-driven, translational research, leveraging clinical studies to identify targets for medical intervention and to generate insight that meaningfully impacts patient care.

This position provides direct mentorship from faculty with deep expertise in neuroimaging, genetics, and neurodevelopment, along with access to state-of-the-art imaging technology and advanced computational resources. The fellow will have structured opportunities for professional development, including conference travel and grant-writing experience, within a highly collaborative environment that bridges basic neuroscience and clinical research. In addition to leading the primary neuroimaging trial, the fellow will have opportunities to collaborate on projects within the Human Neural Circuitry program and to work with Dr. Karl Deisseroth’s lab on genetic models and cutting-edge intracranial electrophysiology.

We are seeking a postdoctoral researcher to lead the neuroimaging component of a longitudinal, pediatric drug trial in Neurofibromatosis type 1 (NF1). In this role, you will acquire and analyze multimodal MRI data from children with NF1 using the Siemens Cima X scanner at Stanford’s Lucas Center for Imaging. The position sits at the intersection of advanced neuroimaging methods, longitudinal clinical research, and translational neuroscience, with direct relevance to improving outcomes for children with neurodevelopmental conditions.

Core responsibilities 

You will have end-to-end responsibility for neuroimaging methodology, including:

• Designing, adapting, and overseeing multimodal MRI acquisition protocols (structural, diffusion, functional MRI), with attention to pediatric constraints

• Ensuring longitudinal consistency and data quality, including parameter tracking, protocol versioning, and rigorous QC frameworks

• Implementing, validating, and extending image-processing and analysis pipelines for individual-, longitudinal-, and group-level inference

• Applying advanced statistical models for longitudinal data (e.g., mixed-effects models, missing-data handling)

• Integrating imaging outputs with behavioral and clinical measures in collaboration with domain experts

• Managing structured datasets in REDCap and maintaining reproducible, version-controlled workflows

• Leading methods-driven manuscripts and presenting technical work at scientific conferences