We are seeking applicants for a post-doctoral fellowship position to analyze and collect large imaging datasets using computational approaches, such as deep learning. The aim is to integrate findings across imaging and other modalities.
Candidates with a strong computational background (e.g. engineering, physics, mathematics, or statistics) who are interested in brain development and psychopathology are particularly encouraged to apply.
Our research focuses on understanding the brain-behavior interface in young people with mental illness, particularly anxiety and depression. We apply multiple modalities (ranging from BOLD-signal MRI to arterial spin labelling, DTI, and MEG) to data collected longitudinally in experimental or intervention designs. The successful candidate would work with both of us and use resources from across the Department for their work.