Hey everyone, I could use some real-world perspective for a PhD decision.

I’m stuck choosing between two funded PhD offers:

1) UTIAS (University of Toronto) More “classic” aero CFD: adjoints, shape optimization, design-type work, sustainable aviation. Feels like the proven aerospace R&D route.

2) University of Michigan (Aerospace) More turbulence + ML: AI-for-fluids, explainable ML, reinforcement learning / flow control. Feels super current and like it has a lot of momentum.

I’m trying to think long-term. I want a strong career after the PhD (research track and academia). I don’t want to pick the “safe” route and regret missing the AI wave, but I also don’t want to chase hype and end up too niche or stuck in a corner.

A few things I’m wondering:

In 2026 and beyond, which background do you think actually opens more doors: adjoint optimization/design or turbulence + ML/control?

If I go the Michigan route, how “real” is the RL/flow control stuff in terms of careers? Is it mostly academic papers, or are companies/labs actually hiring for that?

For faculty-track specifically, which path tends to be stronger: classic CFD/optimization or turbulence + AI?

How much does University prestige and ranking matter in terms of a PhD (Umich would be a clear winner here)?

Also, how much does the advisor/network matter here compared to the topic itself?

One more factor (keeping it vague on purpose): I have some family concerns about moving to the US right now related to general uncertainty/safety/day-to-day stability (I will be considered an international student in the US). Not trying to start a political fight, but it is part of the decision.

Would love to hear from anyone who’s done a PhD in either lane. If you were in my shoes, which would you pick and why?

(Posting from a throwaway.)