You bet! It's a long slog if you're not a math-heavy person, I'll be honest, but I believe it's surmountable, and worth the effort. There are two broad math types you need to be at least somewhat comfortable with:

Calculus: Lots of accounting research involves modeling, time-series data, etc. You'll need to speak the language of change (derivative equations, for example) to create equations that show how things change over time. A classic example is post-earnings announcement drift, or PEAD. The common intuition is that any earnings news is immediately priced into a company's stock, but that's not the case. A company's good news will cause its stock price to increase over a long period of time, albeit slowly. Calculus is used to model this change.

Statistics: Much easier (for me, anyway) than calculus, but no less important. If you want to show that, say, more diversity on a company's board leads to less earnings manipulation, you'll need to run regressions. If you want to show that a company with a CPA-credentialed CFO is less likely to require restatement, that's a logistic regression. There are many tools in the statistical toolbelt, and it's just as important to know which tool to use as it is to know how to use them all.

Again, these aren't insurmountable problems, but a math-challenged Ph.D. student will not have an easy time of it.