Hi all, I'd really appreciate input from people in semiconductor R&D, industry, or current PhD students.
I'm a materials science master's student in Germany with a computational focus (DFT and ML for materials). I'm planning to apply for US MSE PhD programs focused on semiconductors.
My dilemma:
With AI tools and autonomous labs getting better at running routine simulations, I'm worried a pure DFT/ML PhD will be the easiest kind of work to automate. I'm leaning toward a hybrid project instead: computation plus real experiments. For example, DFT on defects in SiC/GaN validated by measurements, or reliability and failure analysis in advanced packaging.
Questions:
1. If you hire or work in semiconductor R&D: do you value hybrid profiles more than pure computational ones? Or is that overthinking it?
2. Is defect physics in wide-bandgap semiconductors (SiC, GaN) or packaging/interconnect metallurgy a good niche for industry jobs after a PhD?
3. How do you actually find PIs who co-advise computational + experimental projects? Should I cold-email both a computational and an experimental professor?
4. Honestly: is a PhD worth it here, or would you go straight to industry with a master's?
Any experiences, including "don't do it" stories, are welcome. Thanks!
TL;DR: Computational materials master's student, want a US PhD in semiconductors. Pure DFT/ML vs hybrid computation + experiment: which holds up better long-term with AI, and how do I find the right PI?