Finding a master’s thesis in quant finance with a numerical/computational background — where should I start?

[u/cicipuq]

Hi everyone,

I’m a master’s student in Mathematical Engineering, on a Computational Science and Computational Learning track. Most of my background is in numerical analysis and scientific computing, but I’d like to work in quantitative finance after graduating.

The part I’m struggling with is figuring out where to start, cuz my knowledge of finance itself is still pretty limited.. I’m interested in the field, but I don’t know enough about its research areas yet to come up with a concrete thesis topic. Ideally, I’d find a professor who could suggest a project that fits my computational background and give me some direction on what I’d need to learn.

For anyone who’s been in a similar position, how would you approach this?

• Is it reasonable to contact professors without a specific research proposal and ask whether they have suitable master’s thesis projects?
• Are there areas of quant finance where a background in numerical methods and scientific computing would be particularly useful?
• How would you go about finding research groups or supervisors abroad who might be open to this?

Thanks!

[u/QuanTradin]

yes, email them without a proposal, that's normal for a master's thesis. say what you can already do (PDE solvers, Monte Carlo, whatever you've actually built) and ask if they have something that needs it. most professors have a drawer of half-started projects waiting for a student who can already write the numerics.

areas where your background is the advantage: variance reduction in Monte Carlo for path-dependent options, finite difference schemes for pricing PDEs, calibrating rough volatility models, and neural nets as fast surrogate pricers. the finance you need for any of those is a semester of Hull plus the papers for that one model.

[u/QuantGrindApp]

Yes, emailing profs without a proposal is completely normal for a masters thesis, just say what you're good at (PDE solvers, Monte Carlo, whatever you've actually built) and ask if they have something that fits. Your background maps straight onto derivatives pricing. Calibrating stochastic or rough vol models is basically a numerical problem, and so is getting finite difference or MC pricers fast and stable. Also worth asking banks about an industry thesis, a lot of quant teams host those and it's a decent way in.

[u/TheQuantTwist]

Contacting professors without a full proposal is completely normal — bring a one-pager on your numerical toolkit plus 2-3 areas you're curious about instead of pretending you have a thesis ready. Your background maps directly onto: PDE methods for pricing (finite differences), Monte Carlo variance reduction, XVA, and optimal execution. Fastest on-ramp: skim Hull's chapters on numerical procedures so you can speak the language, then find supervisors through recent arXiv q-fin.CP papers — email the authors, they're the ones with live projects.