-
engineering and clinical physiology. Projects may involve signal quality assessment, artifact detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning
-
, electrical engineering, cognitive science, applied mathematics, physics, or a related field. Strong computational background and hands-on experience building AI/ML models. Expertise in modern architectures
-
to Submit Application Materials: Interested candidates should submit a single combined PDF that includes (1) a letter of interest with a brief summary of previous research experience (~1 page), (2) a current
-
field Demonstrated expertise in one or more of the following areas: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including
-
and settings, including children who are deaf and hard-of-hearing. Required Qualifications: Ph.D. in Psychology, Linguistics, Cognitive Science, Electrical Engineering, Computer Science, Education
-
experience (~1 page), (2) a current CV/resume, and (3) contact information for three references. Please email application materials to Michael Schumacher at [email protected] and cc Kristoffer Nguyen
-
care for patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
-
collected through ongoing SCEC programs. This data will support the postdoc's work developing and deploying multimodal models to improve the iFIND tool, extending its current text-based approach. Primary
-
designing machine learning pipelines, building web applications or tools, and creating and maintaining visualization dashboards. Trainees should be comfortable with: · SQL, R, and Python
-
completely automated, lightly gamified, online assessments that are grounded in ongoing cognitive neuroscience research and validated against the current “gold standard” of standardized, individually