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The Coordinator, Research Data and AI Development plays a pivotal role in advancing the research capabilities of the College of Engineering by developing and implementing Artificial Intelligence (AI)-driven tools and datacentric solutions. This position supports strategic decision-making and fosters innovation, ensuring the College keeps pace with the rapid, exponential growth of its research enterprise. In collaboration with faculty, researchers, and leadership, the Coordinator leads efforts to design, optimize, and deploy cutting-edge AI applications that enhance research methodologies, streamline data management, and provide actionable insights for institutional advancement. The role involves overseeing data strategy, maintaining robust research infrastructure, and ensuring compliance with best practices in AI and data governance.

 

Development and Coordination of AI-Driven Research Tools: Designs, develops, and maintains AI and machine learning tools to support faculty research, data analysis, and predictive modeling. Collaborates with faculty and research groups to prototype, refine, and scale AI applications that accelerate research productivity and innovation. Evaluates and integrates open-source and commercial AI platforms to optimize research methodologies and expand analytical capabilities. Develops custom dashboards, interactive visualizations, and automated reporting tools to enhance decision-making, performance tracking, and research impact assessments.

Research Development, Impact, and Funding Support: Provides technical and strategic expertise to faculty on AI, big data, and analytics-driven research proposals, enhancing grant competitiveness. Assists in crafting and refining data management plans (DMPs) and AI methodology sections for grant applications to ensure compliance with funding agency standards. Applies advanced analytics, impact modeling, and dynamic visualization techniques to help research teams effectively showcase the societal, technological, and economic significance of their work. Identifies emerging AI research funding opportunities and supports faculty in aligning their proposals with evolving industry and academic trends.

Research Data Infrastructure, Governance, and Compliance: Oversees secure, ethical, and compliant data management practices, ensuring adherence to institutional, legal, and funding agency requirements (e.g., GDPR, HIPAA, NSF, NIH). Develops and maintains robust, reproducible, and auditable data pipelines to ensure data integrity and accessibility for AI models and research analytics. Partners with Engineering Network Services, central IT, campus data governance, and research administration units to align research data security and governance strategies with institutional policies. Implements best practices for AI model transparency, explainability, and responsible AI deployment in research applications.

Talent Development and Training: Designs and leads training sessions, workshops, and bootcamps on AI tools, data ethics, open science, reproducible research, and advanced research data management. Mentors students and researchers in AI-enhanced, data-driven methodologies to cultivate expertise in computational research and analytics. Facilitates interdisciplinary collaboration to bridge AI technologies with diverse research fields, fostering innovation and cross-sector impact.

Ready to lead and shape the future of higher education? Apply today! War Eagle!

Master’s Degree in Computer Science, Machine Learning/Artificial Intelligence and Data Science and 5 years of Experience using and developing ML/AI-based tools (e.g. large- and small-language models) and models for research and experience in data science and graph theory. Experience in user interfaces that enable non-experts to utilize the developed tools. Experience shows progressive increase in productivity and levels of responsibility.

or

PhD in Computer Science, Machine Learning/Artificial Intelligence and Data Science and 2 years of Experience using and developing ML/AI-based tools (e.g. large- and small-language models) and models for research and experience in data science and graph theory. Experience in user interfaces that enable non-experts to utilize the developed tools. Experience shows progressive increase in productivity and levels of responsibility.

 

Understanding of AI frameworks, neural networks, deep learning, NLP, and computer vision.
Expertise in statistical modeling, predictive analytics, and big data methodologies.
Familiarity with best practices for data collection, processing, storage, and sharing in academic research.
Knowledge of funding agency requirements (NSF, NIH, etc.) and how to structure competitive grant
proposals involving AI and big data.
Understanding of regulations such as GDPR, HIPAA, and institutional policies on research data security.
Knowledge of current, evolving AI technologies, research methodologies, and industry standards.
Expertise in dashboards, interactive visualization, and automated reporting platforms.
Proficiency in Python, R, TensorFlow, PyTorch, or similar AI/ML tools.
Ability to work with SQL, NoSQL, or graph databases for research data storage and retrieval.
Ability to assess research needs and develop tailored AI-driven solutions.
Strong interpersonal skills to work effectively with faculty, researchers, and leadership.
Capacity to anticipate emerging AI trends and integrate cutting-edge tools into research practices.
Skill in bridging AI technologies with diverse research fields for transformative impact.