I recently had an epiphany – I’m more excited about the coding and machine learning aspects of my PhD than the physics itself. As a 2nd-year ChemE PhD student working on granular media with ML, I’ve come to realize that building models, debugging, and testing new architectures is what truly gets me going. However, when it comes to digging into the physical interpretation, I find myself losing interest.
This got me thinking – what skills should I develop to transition into a more computational or ML-heavy role after my PhD? I don’t have a CS background, and my coding skills are mostly self-taught. I’ve heard that learning formal CS concepts like algorithms and software design is crucial, but I’m not sure where to start.
If you’ve gone down a similar path, I’d love to hear about your experiences. What skills did you focus on developing, and how did you make the transition? Were there any particular resources or courses that helped you along the way?
For me, the goal is to move into a field like scientific computing, data-driven modeling, or applied AI for physical systems. I’m excited to start exploring these areas and seeing where my passions take me.
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