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The Senior Research Engineer is a member of the Neuroimaging and Brain Dynamics Lab, a research laboratory within the Vanderbilt Lab for Immersive AI Translation (VALIANT) and is a key individual contributor responsible for advancing AI tools for neuroimaging data analysis. This position leads research projects, manages computational pipelines, mentors junior researchers, and fosters collaborations across disciplines. Reporting directly to the principal investigator of the Neuroimaging and Brain Dynamics Lab, the Senior Research Engineer frequently interacts with cross-institute partners, students, and international collaborators to drive innovation in neuroscience and AI. The Senior Research Engineer will develop machine learning approaches for multimodal neuroimaging data, optimize research workflows, lead Open Science initiatives, and contribute to funding proposals. They will play a crucial role in bridging neuroscience and AI, ensuring the lab's research is at the forefront of technological advancements while maintaining rigorous scientific standards.

About the Work Unit: VALIANT is a pioneering center at Vanderbilt University, dedicated to accelerating innovation at the intersection of artificial intelligence and scientific discovery. Our approach is built on three pillars: cultivating expertise, fostering cross-disciplinary synergy, and making strategic investments. We empower researchers with deep knowledge in informatics and AI, promote knowledge flow across traditional academic boundaries, and create a structured ecosystem for discovery. Our agile decision making model allows us to rapidly identify potential, prototype solutions, advance research, and deploy investments. We continuously refine our focus based on real-world feedback, actively engaging with the VALIANT community to drive intellectual and societal impact.

Key Functions and Expected Performance:

• Lead research projects in AI tools for multimodal neuroimaging data analysis, including: Developing ML approaches for integrating and analyzing fMRI and physiological signals, Adapting state-of-the-art ML methods for time-series data to fMRI research, Creating ML models for estimating functional parcellations from structural brain features

• Manage and optimize the lab's computational pipelines for efficiency and reproducibility in data processing, ML model development, and downstream analysis

• Identify resources and initiate cross-institute collaborations (e.g., with DSI and ACCRE) to streamline workflows

• Mentor junior researchers in the interdisciplinary lab, supporting project management, providing technical guidance, and leading discussions on advanced machine learning topics Play an active role in writing and formulating funding proposals at the intersection of neuroscience and AI

• Lead Open Science initiatives and develop mechanisms to ensure the lab follows best practices in accessible, transparent research

• Chair the annual "BrainHack" hackathon and coordinate the Vanderbilt hub of the international "BrainHack School" educational course

• Provide expertise and advice to collaborators on topics in machine learning and fMRI, fostering research collaborations on campus and globally

• Develop and disseminate open-source software for visualization, automated artifact detection, and improvement of physiological signal quality

• Contribute to multimodal representation learning with applications to neuroscience (e.g., emotion recognition, brain state decoding, event segmentation)

• Create interactive visualization tools for fMRI data

Supervisory Relationships: This position does not have direct supervisory responsibility but is expected to mentor students and junior researchers; this position reports administratively and functionally to the principal investigator of the Neuroimaging and Brain Dynamics Lab.

Education and Certifications

• PhD in Computer Science, Neuroscience, Biomedical Engineering, or a related field is necessary.

Experience and Skills:

• At least one year of postdoctoral or equivalent research experience is necessary.

• Extensive experience in machine learning, particularly as applied to neuroimaging data analysis, is required.

• Demonstrated ability to manage multiple projects simultaneously is required.

• Experience initiating and managing cross-disciplinary collaborations is required.

• Excellent communication skills, both written and verbal, are required.

• Strong task orientation and ability to meet deadlines is required.

• Proficiency in programming languages commonly used in data science and neuroimaging (e.g., Python, R, MATLAB) is required.

• Experience with fMRI data analysis and related software tools is strongly preferred.

• Knowledge of physiological signal processing is preferred.

• Experience in open-source software development and version control systems (e.g., Git) is preferred.

• Familiarity with high-performance computing environments is preferred

Commitment to Equity, Diversity, and Inclusion

At Vanderbilt University, we are intentional about and assume accountability for fostering advancement and respect for equity, diversity, and inclusion for all students, faculty, and staff. Our commitment to diversity makes us who we are. We have created a community that celebrates differences and lets individuality thrive. As part of this commitment, we actively value diversity in our workplace and learning environments as we seek to take advantage of the rich backgrounds and abilities of everyone. The diverse voices of Vanderbilt represent an invaluable resource for the University in its efforts to fulfill its mission and strive to be an example of excellence in higher education.

Vanderbilt University is an equal opportunity, affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran, or any other characteristic protected by law.

Please note, all candidates selected for an offer of employment are subject to pre-employment background checks, which may include but are not limited to, based on the role for which they have been selected: criminal history, education verification, social media review, motor vehicle records, credit history, and professional license verification.