Florida State University has launched a 2026 AI Bootcamp series that runs from Aug. 6 through Oct. 9, offering free training on artificial intelligence applications in research to students, faculty, and staff. The series, organized by the Department of Chemistry and Biochemistry in the College of Arts and Sciences, covers topics ranging from agentic AI and data analysis with large language models to mental health considerations and career planning.
The bootcamp takes place in the Kroto Auditorium at FSU's Chemical Sciences Laboratory and has drawn participants from departments across campus. Guest lecturers, interactive workshops, panel discussions, and guided debates make up the programming, which was organized by Cottrell Family Professor of Chemistry Oliver Steinbock and Assistant Professor of Chemistry and Biochemistry Wen Zhu.
Speakers and topics
Featured speakers come from FSU departments including Chemistry and Biochemistry, Scientific Computing, Computer Science, and the Interdisciplinary Data Science Master's Degree Program, plus FSU Libraries, Information Technology Services, and the School of Information. Visiting speakers include experts and returning FSU alumni from Emory University, Carnegie Mellon University, the University of Florida, Pacific Northwest National Laboratory, Google X, and the AI-driven platform Synfini.
The schedule is designed for researchers who want to understand AI's practical role in laboratory work and teaching. Sessions address how AI tools handle research data, what limits large language models have in scientific contexts, and how researchers can integrate these systems into their workflows. For those who cannot attend in person, the full program and registration details are available on the bootcamp website.
Researchers looking for structured training beyond the bootcamp can explore the AI Learning Path for Research Scientists, which covers similar ground in a self-paced format. The bootcamp's focus on hands-on applications aligns with broader AI for Science & Research resources that address the specific demands of academic and laboratory settings.
Why this matters for science and research professionals
Research institutions are increasingly expected to use AI tools, but most scientists have had no formal training in how these systems work or where they fail. A program like this one gives researchers a way to evaluate AI critically before adopting it in their own work. For working scientists, the practical takeaway is straightforward: the ability to assess when an AI tool is reliable - and when it is not - is becoming a core research skill, and structured training programs are starting to treat it that way.
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