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Convergence research and training in computational bioengineering: a case study on AI/ML driven biofilm-material
Research Square
|September 18, 2023
Summary
This study introduces a convergence training framework to bridge the gap between graduate education and multidisciplinary research expectations. The program successfully integrated data science, material science, and bioengineering, preparing students for convergence research roles.
Area of Science:
- Bioengineering and Material Science
- Computational Biology
- Data Science and Artificial Intelligence
Background:
- Traditional research disciplines operated in silos, leading to a gap in graduate preparedness for emerging trans-disciplinary and convergence research.
- Current graduate curricula often fail to equip students with the necessary skills for multidisciplinary research environments, particularly in fields like bioengineering and material science.
- The increasing complexity of scientific challenges necessitates a move towards convergence research, requiring novel training approaches.
Conclusions:
- The developed convergence training framework, utilizing problem-based learning, effectively consolidates and integrates convergence research principles into graduate curricula.
- Learner feedback indicates a successful experience in bridging expertise gaps and fostering collaboration in transdisciplinary projects.
- The program demonstrates a viable model for preparing students for careers in rapidly evolving, interdisciplinary scientific fields.
