Assistant Director, MEG Facility

Assistant Director, MEG Facility Vanderbilt University - 3.9 Nashville, TN Job Details Full-time 10 hours ago Qualifications Data transformation pipeline development Signal processing User training (technical support) Scientific research papers Maintaining data pipelines Doctor of Philosophy Neuroimaging Research data analysis Training & development Manuscripts for peer-reviewed journals Full Job Description The Assistant Director, MEG Facility provides scientific, technical, and operational leadership for the Magnetoencephalography (MEG) facility within the College of Connected Computing. This role integrates facility oversight, advanced neuroimaging expertise, research infrastructure development, and independent scientific leadership. The incumbent ensures the reliable operation of the MEG system, develops and maintains advanced data acquisition and analysis pipelines, trains researchers to independent operator status, facilitates cutting-edge interdisciplinary research and develop their own research agenda. In parallel, the position contributes to high-impact publications, grant development, and the strategic growth of NeuroAI research at Vanderbilt University. About the Work Unit The College of Connected Computing (CCC) is Vanderbilt's interdisciplinary hub for advancing research and education at the intersection of computing, neuroscience, engineering, and data science. The MEG facility serves as a core platform for high-resolution measurement of brain dynamics, supporting collaborative research across NeuroAI, cognitive neuroscience, and related domains. Key Functions and Expected Performance MEG Facility Leadership and Operations Direct day-to-day operation, maintenance, and quality assurance of the MEG system Ensure compliance with safety, regulatory, and data governance standards Manage scheduling, usage, and optimization of facility resources Interface with vendors, engineers, and institutional support services Training and User Certification Train and certify researchers (faculty, postdocs, students) as MEG operators Develop training protocols, documentation, and best practices Provide ongoing technical support for data acquisition and experimental setup Promote high standards of autonomy and reproducibility across users Data Infrastructure and Analysis Pipelines Develop, implement, and maintain scalable MEG data processing pipelines Organize and manage large-scale neuroimaging datasets Ensure reproducibility, standardization, and integration with computational workflows Support advanced analytical methods and integration with AI models Research Facilitation and Collaboration Collaborate with investigators to design and implement MEG experiments Provide expertise in experimental design, signal processing, and analysis Support interdisciplinary grant proposals and research initiatives Expand the methodological capabilities of the MEG facility Independent Research Program Develop and lead an independent research programme using MEG Publish in high-impact peer-reviewed journals Contribute to grant applications as PI or Co-I Advance methodological and conceptual innovation in NeuroAI Service and Strategic Development Contribute to institutional service and facility strategy Participate in committees and collaborative initiatives Other duties as assigned Supervisory Relationships This position does have supervisory responsibility and reports administratively and functionally to the PI. Education and Certifications PhD in Neuroscience, Cognitive Science, Biomedical Engineering, or related field is necessary.

Experience and Skills Necessary:

5 to 7 years of relevant experience Demonstrated expertise in MEG acquisition and analysis Experience developing neuroimaging pipelines and handling large datasets Track record of peer-reviewed publications Experience training or mentoring researchers Strong programming skills (Python, MATLAB, or equivalent)

Preferred:

Experience managing or coordinating a neuroimaging facility Expertise in advanced MEG analyses (e.g., source reconstruction, time × frequency analyses, connectivity, time-resolved decoding, information-theoretic approaches, reverse engineering of linear and nonlinear computations) Experience integrating neuroimaging with computational modeling or AI Track record of supervising interdisciplinary research Track record of methodological or tool development