We are seeking a computer science/machine-learning postdoctoral fellow to develop algorithms for automatic dystonia diagnosis, risk prediction, and treatment efficacy, building on the DystoniaNet platform using brain MRI data from patients, other movement disorders, and healthy controls.
The fellow will join a multidisciplinary team of neuroscientists, neurologists, laryngologists, and geneticists to advance the clinical application of DystoniaNet, focusing on data processing, algorithm development, clinical translation, collaboration, and dissemination through publications and presentations.
Qualifications include a PhD in computer science, neuroscience, or biomedical engineering, expertise in machine learning, neuroimaging data processing, programming (Python, Matlab), cloud platforms, and strong communication skills. The role offers opportunities for independent and collaborative research, with career advancement prospects in academia or industry.