PHD in computational math (large-scale network modeling) or NYU math fin.

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I'm an international student (undergraduate) in math and physics.

There is a dilemma, a PhD in computational math (Top 3 IVY, can be focused on large-scale network modeling) or NYU Math.Fin. My career plan is a Quant or researcher position in a quantitative trading group. And my long-term goal is to run my own business (hedge fund?) in my hometown (a fast-growing global financial center). So I really don't know which one to pick, PhD (solid quantitative skills) or math fin. (working experience)?

Another question: Is it difficult for a person who works as a quant for 1-3 years to be admitted to a PhD program in financial engineering?

Any replies are appreciated.
 
for long term prospect op should go for ivy math... there are several reasons. for a fresh graduate there are still plenty of time to study and there is no deed to rush to a decision what to do for the next twenty years. a phd from an ivy school can jump to quant according to personal plan without one mfe year. finally, I don't think how much valuables could be studied in just one year.. for mfe, money is the only thing that provides momentum and that's all. of course it depends on op's career plans and interest. phd does take time.
 
for long term prospect op should go for ivy math... there are several reasons. for a fresh graduate there are still plenty of time to study and there is no deed to rush to a decision what to do for the next twenty years. a phd from an ivy school can jump to quant according to personal plan without one mfe year. finally, I don't think how much valuables could be studied in just one year.. for mfe, money is the only thing that provides momentum and that's all. of course it depends on op's career plans and interest. phd does take time.

Doing research is fine for me. Besides, I want to have a position in a prominent hedge fund. Thank you for you replies.
 
Ph.D, no student loans. Remember, many professors not only teach, but work for large companies on the side
 
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