Computational Foundations of Biomedical Simulation (GE, BB, MC)

Description from DukeHub

This is an applications course highlighting the use of parallel simulation in solving biomedical problems. The goal is to provide a foundation in the tools and methods for building and implementing applications for parallel architectures including source-code control and testing frameworks. Topics will include computational abstraction, performance profiling and analysis, scalability, thread- and core-level parallelism, I/O, and visualization. Prerequisites: BME 302L or BME 307 or graduate standing.

Skills

• lab workhas a lab component
• programmingAI review: "building and implementing applications for parallel architectures"
• data and statisticsAI review: "performance profiling and analysis"

AI-resilient: 1 of 5 1/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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