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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical
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multiscale approaches to uncover how physical forces (external and intracellular) reprogram subcellular architecture and gene expression, advancing nanotherapeutics and regenerative strategies. We invite
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statistical and epidemiological methods Working experience with data analysis software, such as Stata, SPSS, R, or Mplus Experience in spatial and temporal data analysis using GIS, including RStudio/Python
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