Building an AI-ready RSE Workforce
Ying Zhang (1), Matthew A. Gitzendanner (1), Dan S. Maxwell (1),, Justin W. Richardson (1), Kaleb E. Smith (2), Eric A. Stubbs (1), Brian J., Stucky (1), Jingchao Zhang (2), Erik Deumens (1) ((1) University of Florida,, (2) NVIDIA)
TL;DR
This paper discusses the impact of AI on research software development and presents strategies to prepare the workforce for AI-driven changes in the field.
Contribution
It offers insights into current challenges and opportunities AI presents for research software engineers and describes the University of Florida's approaches to workforce preparation.
Findings
AI is transforming research software development processes.
Strategies are being developed to train engineers for AI integration.
The paper highlights the importance of adapting workforce skills to AI advancements.
Abstract
Artificial Intelligence has been transforming industries and academic research across the globe, and research software development is no exception. Machine learning and deep learning are being applied in every aspect of the research software development lifecycles, from new algorithm design paradigms to software development processes. In this paper, we discuss our views on today's challenges and opportunities that AI has presented on research software development and engineers, and the approaches we, at the University of Florida, are taking to prepare our workforce for the new era of AI.
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Taxonomy
TopicsScientific Computing and Data Management · Artificial Intelligence in Healthcare and Education · Machine Learning in Materials Science