Latest reviews

The new program director has been making positive changes to the program, there are enough good career resources for students to take advantage of, like company visits, info sessions, and internal career development forums. MAFN shares a lot of career activities with the CBS MSFE and IEOR MFE. The program gets people ready early in the summer by hosting multiple events, we have met with the students from the upper class who have secured a job to talk to us, and sessions held by faculty on every issue like refining resumes, interviews, LinkedIn profiles, and etc. Curriculums are also upgraded to better meet with industrial needs (Stochastic Processes cover more real interview style questions on class). I have heard concerns about courses being too mathematical and does not cover enough coding, however, it's actually easy if you want to register courses from the Engineering school. The Maths courses are also very important. The program is also very lenient when it comes to waiving mandatory courses so that you can really take what you are interested in. It's not perfect yet, considering the relatively large cohort, it's really hard to get everyone engaged, definitely more efforts could be made, but the program is moving in a very promising direction.

It is my supreme honor and privilege to be part of the MFM program. Despite its intensive 12-month structure, the program delivers exceptionally rigorous quantitative training taught by some of the most distinguished professors in Canada and even worldwide. The curriculum is designed to build a deep foundation in stochastic calculus, assets (including derivatives such as exotic options even!) pricing & valuation, mordern portfolio optimization, machine learning and statistics, ensuring that students are equipped with both the technical expertise and the practical intuition required in today’s complex financial landscape.

After two academic semesters is a semester of industrial co-op, the cornerstone of the MFM experience. Leveraging the program’s strong connections with its alumni network and leading institutions across the Canadian financial sector, students have access to a wide range of internship opportunities. These placements span front-office quantitative research, trading support, risk analytics, model development, and model validation across banks, pension funds, and financial consulting firms. This structure enables students not only to apply what they have learned in real-world environments but also to gain industry insights, refine their professional skills, and build meaningful networks.

The combination of academic excellence, practical training, and deep industry integration makes the program an exceptional platform for aspiring financial engineers and quantitative analysts.

I am deeply grateful for the knowledge and connections I gained through the MFM program. I especially want to express my sincere appreciation to all the professors who guided me throughout this short yet profoundly meaningful one-year journey. I am fully confident that their dedication, mentorship, and commitment to students will continue to benefit many more aspiring financial professionals in the years ahead.

Pros
1. The program itself allows you to get in touch with my brilliant minds for example, MBA, Executive MBA, Scholars, Professors with Distinctions, and supportive alumni
2. Careers Aspects are supportive and positive, when you put the name of the school, your interview(at least for intern) is responsive.
3. Courses are flexible and centric on IB and AM, also some Entrepreneurial stuff too.

Cons.
1. Career Services are not your dependent. You need to find the job yourself and sometimes their support is kinda like Psychiatrist who gives you positivity to move on. Of course, they are still very useful for helping you connecting alumni and resume, CV, interview, stuff like that.
2. If you are wondering whether it is Quant or not, i would say "Quantamental", traditional with some Quant but defintely not as heavy as Fin Math program. This means you need to acquire some Quant knowledge so that you could excel in finding a Quant job with this degree.
3. The academics and industry needs are a bit torn apart. That means what you learn might not be used for industry. But it is useful for building senses of the reasons why this model or this techniques are used. They did pretty good on that.

SO, this is a new program, lots of its directions are undecided. Just like startup, you will end up some disappointments in this program which makes sense since it is new. and you need initiatives to build your own portfolios. But i do see this program will be a very impactful program that might benefit you in the future. If you are advanturers who want to try something firsthand, go for it. If you afraid of uncertainty or disappointments, maybe other mature programs could be suitable for you but each of them have disappointments too.

First of all, I'm still a current student in the program. I enrolled about 1.5 months ago, so this review is based on my experience so far. I'll update it with more insights once I've completed the program. I'll try to be as objective as possible, and I'm writing this with the goal in mind that it can help future prospective students.

Overall, it's definitely a great program. I can see why it would be ideal for someone aiming for a PhD, but at the same time it also prepares you well for industry roles*.

For context, I did statistics in my undergrad, and I worked in some tech companies in Southeast Asia for three years before going for this program. I was accepted into UCL's Financial Mathematics, Edinburgh's Computational & Mathematical Finance, and NUS's MQF. I chose Warwick because, judging from the modules alone, in my opinion it had the most well-designed structure for whatever path you want to take, either becoming a quant or preparing for a PhD. The curriculum has both breadth and depth, and Warwick has a strong reputation in both mathematics and finance.

You might think, but, the other schools also have a great reputation in math or finance. And yes, that's true, but another dealbreaker is that this MSc is a joint program between Warwick Business School, the Warwick Mathematics Institute, and the Department of Statistics. And after enrolling, this is actually so good because you get a nice mix of things: career support* as in workshops and in 1-on-1 sessions from the business school team, business-school-style facilities and perks like guest lectures from industry experts (for example, we recently had alumni working as a quant in Deutsche Bank sharing their experiences), while still getting rigorous teaching from the maths and stats departments. I think the mix is one of the program's biggest strengths.

That said, this comes with a price, it's not an easy program at all. Expect to spend most of your time studying and having almost no social life (I think this is really the biggest drawback from this program), because it feels like training to be an avatar mastering all elements: maths, finance, and programming. You really need to know why you want to do this, because the workload and intensity really demand strong motivation and commitment.

Now let's go deeper to the modules and curriculum. Even with a solid math background, the program is challenging (maybe an exception if you are going to a really good math program during your undergrad or are already familiar with some stuffs). Before we even arrived in the UK, they provided preparation material in probability theory and finance. Then we began with intense one-week probability theory lectures (9 am to 4 pm, Monday to Friday, they call this Induction Week) to make sure everyone started with the same foundation. This was essential, because you'll need that groundwork for stochastic calculus, and some other modules aside from stochastic calculus also sometimes refer back to measure-theoretic definitions.

In Term 1, we take mandatory modules like Stochastic Calculus, Programming in C++, Financial Statistics, Simulation and Machine Learning for Finance, and Asset Pricing and Risk. In Term 2, we move on to the applications of Stochastic Calculus, continue with Programming in C++, take Financial Econometrics, and choose two elective modules. Term 3 is focused entirely on the dissertation, and they also have some industry projects available in the third term.

The lecturers and professors are excellent as they really know their stuffs. But, in my opinion, in order to learn maths effectively you need three things: good resources (as in books and learning materials etc), good teachers, and enough time. In this program, we have the first two, but the last one is where it gets tough. The pace is extremely fast. It's not impossibly hard, I would say it's still doable, but you definitely have to consistently put in the work and keep up. We've been told by the program leader that we need to treat this program like our full-time job. Good time management and knowing how to set priorities are really the keys to doing well here.

*) They do provide career support, but at the end of the day it really depends on us (as it should be). You still need to put in the effort to apply for quant roles or any roles that you want (obviously). The team can help to give feedback on your CV, do mock interviews, and guide you in figuring out what you actually want to do, but that's it.

I had a really good experience in the program, and I’m genuinely glad I chose MAFN for my graduate studies. It’s definitely an intense and busy year—balancing challenging coursework with the stress of job hunting isn’t easy—but the experience was meaningful and worthwhile.

The coursework covers the key mathematical foundations used in quantitative finance. It’s very math-focused, so there’s less emphasis on advanced coding or computer science, though you can choose relevant electives to build those skills if you want.

On the career side, the advisors pay attention and do their best to help you land a job. They respond quickly and are there when you need interview prep or have questions.

A big part of what made the program special for me was the people. I met classmates who shared similar values, work ethic, and even a similar outlook on life. Working through tough assignments together, spending long hours in the library, and having fun outside class created memories I’ll always appreciate.

The people from the alumni community are also very kind and approachable—willing to share advice, offer referrals, or talk about their own experiences. Many of them genuinely care about MAFN and speak about the program with pride, and you can really feel their willingness to give back.

I’m thankful for the experience, and I sincerely wish Columbia MAFN all the best.

The master offers a complete overview on the core financial engineering areas: stochastic calculus, derivatives pricing, interest rates, quantitative risk management and machine learning.

Pros:
- Extremely complete
- Innovative: besides the classical topics the master offers math/data science courses that offer state of the art techniques. This is no kidding: EPFL true strength is research, and we often studied topics/papers discovered just few years before
- Hands on experience: (almost) every course includes a project (90% of the time with a coding part), which is super useful to get some practical application and are often asked in interviews.
- Math heavy: idk why I often hear people (that don’t attend mfe) saying that the master has little math. I come from a math background and I can guarantee that we do more math than many other masters (like data science or applied math). Check the course description, not the name.

Cons:
- Little room for customization compared to other master: there are around 20-30 credits to fill with electives, and often the choice is constrained due to the schedule
- First semester: it is too “introductory”. Would be nice to have those courses as elective and not mandatory
- Career service (?) Idk if this is a con actually. Financial engineering is a very narrow discipline and it’s ok to have less offers than other masters. It may not have the same “brightness” as the British masters when it comes to apply to uk banks, but many of us got interviews and offers actually.

In conclusion, this master is for those who are interested not only in financial math, but also to be a bit “financially aware”. Is it worth it? To me yes, but if you are interested just in the math it might be better to do math with a minor in FE.

The MFI program at UofT is a highly diverse program with students from actuarial, mathematics, statistics, computer science, and engineering backgrounds. The program's strong reputation makes admission highly competitive, but the experience in the program makes the entire process rewarding.

I have had several recruiters and hiring managers ask why I am interested in quantitative roles instead of insurance, given my background in financial insurance. The insurance component of the program is maybe 20% or less. The majority of courses are centered around pricing theory, institutional derivatives, time series analysis, numerical methods for finance, data science, finance and insurance risk management, etc. This structure gives graduates the ability to pivot across the finance, insurance, consulting, and fintech industries.

Moreover, we have an industrial seminar series where experts with decades of experience in the finance and insurance sectors deliver 2–3-hour lectures on important topics. There is also a professional development course where students are trained in communication, leadership, public speaking, stakeholder engagement, networking, and effective interviewing strategies.

There are extensive career support services and networking opportunities. The internship and outreach coordinators are always available to review resumes and applications, including conducting mock interviews that help better position students to land roles in the industry. We also have a strong alumni community with rich experience that provides mentorship to current students. In fact, the program is designed to help students optimize every opportunity and training. Graduates work at banks, consulting firms, and insurance companies.

If you want strong career-pivoting ability in the finance and insurance space, the MFI program is a great place to start.

Great course overall!

Good academic course structure with great lectures, strong instruction and an excellent range of optional specialisations.

Admissions process was seamless.

Most lectures were excellent, they offered good teaching and quick and helpful responses.

Career support could have been better and the course seem to lack industry connections.

Overall a great course offering a great conversion to Quant.

I completed the MSc Financial Mathematics at KCL, having previously done my undergraduate BSc in Mathematics with Management and Finance at the same university. My decision to pursue this program was heavily influenced by Dr. Ryan Donnelly (who became the programme director later), whose overview of the course content convinced me it was the right fit. The central London location and relatively affordable home fees (~£12,500) compared to competitors like LSE were additional deciding factors. While I ultimately didn't land in quant finance due to the competitive job market and my gaps in algorithmic knowledge, the programme provided solid preparation for those committed to the field.

Application Process and Prerequisites:
Coming from a mathematics background at KCL made the transition easy, but I'd strongly advise future students without prior exposure to financial derivatives to review "Options, Futures, and Other Derivatives" by J. Hull during the summer before starting. This groundwork will be helpful during the academic year, particularly for the compulsory modules.

Modules and Course Content:
The programme structure includes two compulsory modules:
- Probability Theory [7CCMFM01]
- Risk Neutral Valuation [7CCMFM02]
which form the foundation for everything else. While the lectures for these are solid, the assessments are notably more challenging than some students expect.

The quality of modules varies significantly.Personally, I would highlight these modules:
- Numerical and Computational Methods in Finance [7CCMFM06] - exceptionally well-organised and exceptional lecturer
- Stochastic Control [7CCMFM20] - clear structure, interesting content and excellent lecturer
- Machine Learning [7CCMFM18] - relevant and well-taught
- C++ for Financial Mathematics [7CCMFM13] - valuable as a starting point
However, some modules like Statistics in Finance [7CCMFM05] required piecing together content from lectures and video recordings, demanding extra time to organise notes.

Teaching and Learning Experience:
The programme is heavily self-directed, with lectures being more directional than explanatory. Professors provide the framework and direction, but students must deeply understand topics through independent study before attending small-group tutorials. This approach initially felt challenging but eventually developed the self-sufficiency needed in industry.
My advice would be to balance hardcore mathematical modules with programming-focused ones. Given that even standard quant roles (not just hedge funds) require OOP language proficiency, modules like Numerical and Computational Methods in Finance and C++ for Financial Mathematics are invaluable.

Research Project/Dissertation:
The dissertation demands full-time commitment and consists of three parts:
1. Literature Overview - reviewing existing methods and relevant theory
2. Numerical Experiment - demonstrating skills acquired during the programme (fixed for all students)
3. Personal Contribution - extending the topic through different models or practical applications
The supervision is intentionally limited (only 3 meetings allowed in our year), forcing independent research skills. Initially frustrating, I later appreciated this as it provides real-world experience. The topics are generally applicable across quant roles and valuable for CVs.

Career Services and Outcomes:
Career services are limited and most suited for undergraduate students new to internship applications. While career fairs invited companies like Barclays, G-Research and Millennium, these were general networking opportunities. None of my friends heavily engaged with career services. Success still requires grinding through hundreds of math and LeetCode problems independently.
The job market reality is harsh. Despite the programme's quality, the quant finance field is extremely competitive with market surplus. Some peers successfully landed roles at J.P. Morgan or pursued PhDs, but many (myself included) found the combination of competition and gaps in algorithmic optimisation knowledge challenging to overcome.

Overall, this is a solid programme for those certain about pursuing quant finance and willing to dedicate a full year to learning challenging material. The flexibility to combine mathematics with programming across multiple languages is one of the programme's greatest strengths. However, success requires supplementing course work with external preparation (Quant Green Book, LeetCode) to be competitive in the job market. If you're committed to becoming a quant and can handle self-directed learning, this programme offers good value for money and comprehensive preparation.

The joint ETH–UZH Master in Quantitative Finance is the kind of program that quietly belongs in the same conversation as the very best quant degrees worldwide. It combines extremely strong mathematical and statistical training at ETH with a broad and flexible finance curriculum at UZH, all in one of the most important financial centers in Europe. For someone who is serious about a career as a quant or in quantitative asset management, it is a very compelling choice.

The structure of the program is its biggest asset. From the ETH side you get rigorous courses in probability, stochastic calculus, numerical methods, optimization, and machine learning that would not be out of place in a top mathematics or statistics department. These courses are technically demanding and exam preparation is intense, but you come out with a level of comfort around advanced math that is simply not common. On the UZH side you have access to a wide palette of finance, risk management, derivatives, asset pricing, and portfolio management courses.

The curriculum is highly customizable. You can tilt your studies toward mathematical finance and stochastic analysis, toward data science and machine learning in finance, or toward more traditional asset management, risk, and insurance. There is also a very strong path in actuarial and insurance mathematics for those interested in that sector, and a dedicated Portfolio Management Program where students jointly manage a multi million portfolio under faculty and industry supervision. This flexibility allows you to build a very clear profile by the time you graduate, which is critical in a competitive job market.

Teaching quality at ETH has been excellent in my experience. Lectures are structured, detailed, and intellectually demanding, but with professors who care about both rigor and clarity. You learn to think in continuous time models, understand the measure theoretic foundations behind them, and then actually implement numerical methods or machine learning techniques in code. At UZH the quality is more mixed, but with some care in course selection, you can put together a very coherent and high quality study plan.

The student cohort is another strong point. Admission is competitive, and you are surrounded by people with strong backgrounds in mathematics, physics, engineering, computer science, and economics. Group work, coding projects, and exam preparation benefit enormously from this environment. You constantly learn from your peers, and it is motivating to be in a class where people genuinely enjoy discussing both theory and markets. The alumni network is also growing quickly, especially in Zurich and London, and the alumni association is active in organizing events and providing informal career guidance.

In terms of careers, the program gives you a solid platform for roles in quantitative research, model validation, risk management, data driven asset management, and increasingly machine learning in finance. The local job market is competitive, and non EU students face additional hurdles because of immigration rules, but there is a clear path for those who are proactive with internships, networking events, and thesis projects with industry. Many students complete internships or theses with banks, asset managers, hedge funds, insurance companies, or fintechs in Switzerland and abroad, and use those to leverage into full time roles. For those interested in academia, ETH and UZH also put you in a very good position to pursue a PhD in quantitative finance, financial mathematics, or a related field.

The financial side of the program is a major advantage. Tuition fees are very low compared to other top programs in the US or UK, while the academic level is comparable. Zurich is an expensive city to live in, but with part time jobs or internships during the program it is realistic to finance your studies, and the quality of life here is outstanding. The trade off between cost and quality is probably one of the best you can find anywhere for a serious quant degree.

Overall, I would strongly recommend this program to mathematically inclined students who want to build a deep and versatile quantitative skill set for finance. It is demanding, but if you are willing to put in the work, it gives you both the theoretical foundation and the practical exposure you need to compete with graduates from the better known programs in the US and UK. If ETH and UZH continue to invest in career services and international visibility, I am convinced this program will be recognized more broadly as the top destinations for future quants.

Solid content for C++ programming, a great start for people who want to enter the realm. Reason for taking this course is to start in Quant and start to build up algorithms for real-world trading.

The course was VERY instructive and applicable. Having taken a C course in the past, this went even more in depth with the programming aspect rather than just learning about the structure of C. The course picked up swiftly in pace as we got to C++ and everything built on each other. The applications into what we learn as core quant finance (Black-Scholes, Monte-Carlo, SDE, FDM) was fascinating and gives me a good framework for implementation in the future.

I have been using Quantnet as a resource for over a year now, being an aspiring quant trader as an undergraduate in university. By looking through many of the chance me posts, I saw that this C++ certificate can help with developing skills and be a valuable asset in MFE admissions

I didn't have any technical knowledge of finance at all, but thanks to all of the offered courses and the brilliant classmates I can say to have gained a really good understanding of the most useful and needed competences for quantitative finance. The first semester constitutes just the base to understand the contents explained in the second and third semester where the most interesting topics are explained. Furthermore, the environment is really proactive, I met many people with different backgrounds that made me discover so many new things and that made my mind be more open. There's also the possibility to dig into specialized topics within labs, dealing with the most challenged of nowadays, and there's a quite large number of credits that can be obtained from other departments, for example math and physic. The professors are always updated to the new discoveries and help students understand everything. I strongly recommend this course for who is interested in applying mathematics to finance.
About the admission process, it is a bit long but I think it's aligned with the other top masters all around the world.
Career service is large based on the website and the forum where you can directly talk to managers and employs from the firms, including really top ones. The forum also provides many interesting and challenging activities.

Things I liked about the course:
- Great links to industry. Weekly talks quants from the industry that give you insight in to different areas of quantitative finance. I managed to land an internship with a company that was linked with the course and had a very positive experience with them.
- The course is well respected in the industry.
- Many interesting courses that I enjoyed taking, with relevant courseworks which will give you a chance to apply the theory.

Things I didn't like about the course:
- The course generally has more of a focus on sell-side quantitative finance, i.e. derivatives pricing, and will prepare you well for a pricing quant role at an investment bank. I personally enjoy this content, so I found a lot of the courses very interesting and applicable. I interned as a pricing quant and wrote my thesis on an options pricing related topic, so this worked out well for me. However, IMO this is not the ideal course for someone aiming specifically for buy-side QR/QT roles, due to the lack of applied statistics/ML.
- The course is very expensive and becoming even more so.

Overall positive experience.

Courses/Instuction quality: The content itself was quite rigorous. If you put in the time, you are able to come out with quite a comprehensive skillset for quant jobs, even if you began the course with a unrelated undergrad degree. The lecture quality, as with any other course, is very hit or miss, with many lecturers preferring to flick through slides rather than chalk and talk. However, with that being said, all lecturers have impressive research backgrounds, and have planned the content very well to include the beginner knowledge you need to start a entry level quant job.

The courses that stood out to me most were C++ 1 and 2. Each of the two lecturer brought in their own libraries which we had to learn and ultimately complete the coding exam in. This was very useful for me to learn OOP techniques and get a brief feel of how C++ coding is like in an actual job. Although not the main goal of the course, I feel the C++ courses helped improved my understanding of exotic option pricing, in particuliar using LSMC.

Exams: Written exams were quite intense considering multiple courses whose whole grade depends on exam could be combined into a single paper. But the questions were fair and reflective of the content covered throughout the term. We also had take-home exams for deep learning and stats; these were 48 hour long exams containing combination of short answers, long answers, data analysis & other coding questions. And finally, we have two C++ exams, where we had to write functions / complete classes within the lecturers' libraries.

Career service: No dedicated career service for MCF students. There are several careers fair hosted for all Oxford students and you can find interesting companies here. Also iirc there is one career fair more focused for math students.

However, we have weekly industry talks in the first term specifically from banks, hedge funds, consultant firms, etc. who came in specifically for MCF students. These were always followed by coffee/tea session where you could network with presenters. In the third term, there is an option to complete our disseration with a company as part of an internship, and there are many companies (some who came to talks in the first term) that would offer projects for MCF students.

Oxford: Very nice city, great parks, tasty and affordable food in my college. Go to a few formals if you want.

This MSc in Computational Finance helps you enhance your fundamental knowledge required for the quant industry. From courses in probability theory, machine learning to algorithmic trading and market microstructure, there is great flexibility to optimize your learning to fit the buy-side or the sell-side. The professors are truly one of a kind always making sure to adjust their curriculum to keep up with the most relevant advancements in the quant field. The only downside is that apart from the thesis dissertation carried with an industrial partner there is not much exposure in other career opportunities.

Booth’s Master in Finance gave me exactly what I needed to break into capital markets. The coursework is very practical — fixed income, derivatives, and portfolio management directly strengthened my technical foundation and made interview prep much easier. I learned how to discuss markets, structure trade ideas, and think like an asset manager, which helped me land an excellent internship.

Classes are rigorous and highly relevant for anyone targeting asset management or markets roles. Professors bring real industry experience, and the flexibility of electives lets you tailor your track. The program builds strong intuition and quantitative skills at the same time.

Career coaches and alumni were incredibly helpful. I received detailed guidance on interview strategy, technical questions, and networking. The support I got truly accelerated my recruiting process.

A fantastic program if you want to enter asset management or capital markets. Strong academics, a powerful network, and excellent career support.

Baruch MFE’s biggest strength is how early it exposes students to what the quant industry actually needs. The guidance you receive before the program even starts removes much of the information asymmetry and helps you prepare with focus from day one.

The job-search training is highly structured—both technically and professionally—which is especially valuable for international students. The program director is deeply involved in every student’s search and provides hands-on, personalized support throughout the entire recruiting process.

Academically, the courses are practical and often taught by seasoned practitioners, giving you real modeling and engineering experience even if you come from a non-Math or CS background. The program’s strong placement record and respected industry reputation also create a meaningful signaling advantage during recruiting. Combined with its relatively low tuition, the value is exceptional.

I personally really liked the program. My impression is that it’s very well known in Austria and Eastern Europe and it is gaining more recognition across the DACH region. I am currently working in Switzerland alongside colleagues from HSG and ETH and in my view, the program remains highly competitive and certainly does not need to hide behind these top schools.

Some positive facts (that were true at least when I did the programme):

- Big focus on programming in R as the head of the program helped shape the programming language early on and is part of the core developer team. People get really good at it and producing homework, academic reports, presentations and reproducible research. The fact that one used it in almost every course made it a unique experience.
- The coursework covers a broad mix of financial mathematics, applied statistics and corporate finance. With many electives, students can tailor their focus towards quantitative or more traditional finance topics. After the first year, you can choose between an industry track, which includes a semester-long project with a partner company (often leading to job opportunities) or a research track, where you learn to read, write and understand papers in quantitative finance. The university also hosts regular research talks and offers PhD opportunities for those interested in continuing in academia.
- The program has a very international body of students coming from mainly central and eastern europe but also US, Australia, South Korea, India and more.
- There is a big emphasis on group work and you get to interact with your colleagues a lot in class through countless projects.
- The campus is awesome and has a lot to offer in terms of study space resources and leisure activities.
- Vienna is a beautiful city and has a lot to offer for young people.
- The program is basically free for eu citizens and still very cheap for non-eu citizens. So actually top in terms of value for money.


Some room for improvement:
- The student body was comprised of a lot of people with insufficient mathematical background which made it hard for some students to get up to speed.
- The emphasis on group work sometimes enabled people to free ride on the work of their colleagues. If one could focus more on individual assessment, I think it would make it a better and fairer program.
- The student to lecturer ratio was sometimes too large. In general they should cut down on the number of students by 30% and be more selective.
- The biggest disadvantage is that Vienna is not well known for its financial industry. A lot of people seek opportunities elsewhere after finishing the program. Frankfurt, Zurich and London are popular destinations among alumnis.

Here is my personal experience:

1. Faculty

In my experience, the program’s faculty have been excellent. The professors I’ve worked with are knowledgeable, approachable, and often incorporate insights from industry and research into their teaching. Their willingness to engage with students has been one of the strongest parts of the program for me.

2. Teaching and Coursework

The curriculum is mathematically rigorous. Many courses go deep into topics such as convex and non-convex optimization, stochastic calculus, and multivariate statistical methods. This has been extremely valuable for building a strong foundation in the quantitative side of finance.
That said, the level of technicality also means that a significant amount of learning inevitably happens through independent study. Some instructors use Zoom-based lectures, which works for many students, though I personally find in-person discussions more effective. Overall, the program rewards students who are proactive and comfortable taking ownership of their learning.

3. Funding for PhD Students

From what I’ve observed, funding opportunities for PhD students in Quantitative Finance can be limited. Securing full support often requires finding a specialized internship or industry placement, which can be competitive. Faculty members do try to help by recommending students to smaller quant firms, but opportunities are not guaranteed, so prospective students should be aware of this aspect.

4. Career Development Support

Career development is an area where students need to be highly self-driven. Unlike some other programs with established placement pipelines, there isn’t a structured matching or placement process here. Most students take the initiative to apply widely and network independently. While faculty may provide guidance when asked, success largely depends on the student’s own efforts.