i am currently in a math undergrad program and planning on trying to join a masters in math&finance or something along those lines of a quant masters. for quant i know you need to know programming
so my question as a math undergrad whats the best way to bridge this knowledge gap that i have in the programming part of the job (some context i do have some experience with programming but im very rusty its been a few years so its not easy for me plus advanced problems are very hard for me)

any advice is appreciated!

Can someone help me find foreign studies on Finite Mathematics and its applications in real life?

Hi everyone! I’m a Grade 11 STEM student, and we’re currently preparing for our title defense in Finite Mathematics. I’ve been assigned to work on the Review of Related Literature (RRL), specifically the foreign related studies section.

Our research is focused on Finite Mathematics and its application to real-life situations, but I’ve been having a really hard time finding foreign research studies that are directly related to this topic.

If anyone knows of any foreign studies, research papers, journals, or credible sources about:

• Finite Mathematics and its real-life applications
• Mathematical problem-solving in real-world situations
• Applications of discrete/finite mathematics in everyday life
• How students apply mathematics to real-life situations
• Financial mathematics, probability, optimization, or other areas of finite mathematics in practical situations

I would really appreciate it if you could share the links or titles of the studies. 😭

I’m specifically looking for actual research studies or academic sources, not just general websites, since we need credible references for our title defense.

Thank you so much in advance! Any help would be greatly appreciated! 🙏

Hi, I'm applying this year to NYU and I wanted to know how admission for Math+CS works. AFAIK the different schools within NYU have different levels of selectivity, so which school do I apply for that major (Tandon or CAS)? Both? In that case, which school's selectivity do I consider? Thanks so much

I am the equivalent of a high school student, aiming to study in a uni near me to save money. Also aiming for an academic scholarship. If i go to a non target uni, what would be the best major to break into quant? (I am aware it is nearly impossible) while also having a good base to fall back on if i choose to pursue another endeavour

Targeting quant first but also coursework in others ik skillset is diff so I will accommodate with minor and coursework for it just as back up. Is it a good fall back and PE I would do S&T then mba for that. Thanks also school is Columbia Eng.

I'm doing pre-calc I know I'm behind and I just wanna get done with this quickly I've been putting 6 hours every day and I don't know if I should just skim maths or build intuition. I'm broke so I'm tryna wrap it up quickly get a job uk

AI is getting increasingly good at mathematical problem solving and research, which may reduce demand for some traditional mathematical work. Math students should therefore have alternative career paths that still make strong use of mathematical thinking.

Game design is an unusually good fit.

Designing a deep puzzle, strategy game, or simulation can involve combinatorics, probability, graph theory, optimization, algorithms, game theory, geometry, and mathematical modeling. A designer may need to study reachability, reversibility, dominant strategies, state spaces, invariants, difficulty, balance, and how small rule changes affect the entire system.

The bigger obstacle is cultural. Many mathematicians seem to regard game design as entertainment rather than serious intellectual work. Yet mathematicians are perfectly willing to study the mathematics of existing games. Why should analyzing a rule system be prestigious while inventing a good rule system is not?

That distinction may become even less sensible in an AI-heavy future. If AI can increasingly solve problems once they have been formulated, human mathematical creativity may shift toward deciding what problems are interesting and inventing systems worth analyzing.

Math departments could offer concentrations in something like Mathematics and Game Systems, combining traditional mathematics with game design, procedural generation, AI, simulation, and computational experimentation.

Game design should not be viewed merely as mathematics applied to entertainment. At its best, it is the creative construction of mathematical systems.

got shortlisted for gd, and idk what exactly will the topic be, (core or generic), i have gd tonight online, and then tom morning will be the interview if qualified, IDK if to practice GDs/learn about it, or focus on core subj for the interview.

I'm currently, technically, doing an MS in Structural Engineering. For me, my interest has been more towards computational side of mechanics rather than Structural design  or simply using am FEA software (although I do consider it as a backup)

So far I've taken courses in:

- Linear static, and dynamics FEM (soon taking non linear FEM too)

-  Structural Optimization (topology opt. and other general algorithms)

-  Structural Dynamics

-  Structural System Testing and model updation. (Parameter identification and optimization, signal processing)

Now, I plan to take these in the coming quarter:

- Numerical Linear Algebra

- Numerical PDE

- Fracture Mechanics ?

I also volunteered to aid in a RESEARCH in crack growth prediction using Auto-encoder and a (Thermodynamics-informed Latent Space Dynamics Identification) / LSTM surrogate model. It used phase-field-fracture simulation data and HPC resources to complete the whole thing.

What I keep finding myself interested in is not necessarily fracture or SHM specifically, but the computational methods underneath these problems... (does that make sense?)

For example, I'd like to become capable of doing things like:

- implementing (maintaining) numerical/FE method solvers rather than only running an established FEA software.

- developing surrogate/reduced-order models for expensive simulations 

- combining simulation with optimization, uncertainty/stochastic methods (took a course called Random vibrations, so...)

- parallelizing/accelerating scientific codes on CPUs/GPUs

- doing proper verification, convergence studies, benchmarking and performance work

- potentially developing or maintaining actual CAE/FEA solver software

- I'd also like to do all these for other Physics (GR, QM, etc.) simulations too, if possible, one day. 

I'm still interested in the underlying mechanics/physics, so I don't want to become a generic software engineer who happens to have once studied structures. But I'm also increasingly unsure that "structural engineer" describes the career I'm actually aiming for.

I've seen titles such as Computational Mechanics Engineer, R&D Engineer, Solver Developer, Scientific Software Engineer, CAE Software Developer, Research Engineer, Simulation/HPC Engineer, etc., but I'm trying to understand what these careers actually look like from people doing them.

So my main questions become:

1. Which industrial jobs genuinely involve developing numerical methods/solvers or computational tools?

2. Which of those are realistically accessible with an MS? Is there an entry path into solver-algorithm development/R&D without a PhD?

3. If I don't start a PhD immediately after my MS, would an R&D/software role at a simulation company (ANSYS etc.) be the obvious route? What other options would i have?

4. For the kind of work I'm describing, would you recommend a PhD? If so, is it reasonable for the PhD identity to be "computational mechanics/scientific computing" while fracture, composites, structural dynamics, soft materials, etc. serve as application problems rather than choosing one of those as my permanent specialization?

5. What skills most distinguish someone who is actually hireable for solver/scientific-computing work? I'm particularly wondering about C/C++/Fortran, Python, Linux, Git/build systems, MPI/OpenMP/CUDA, PETSc/Trilinos or similar libraries, numerical linear algebra, testing/verification, convergence studies and HPC performance work.

Basically, I'm neither here nor there atp. So I'd really appreciate all sorts of input. Where else do you think I could find answers to these? other subs? Linkedin profiles? 

I’m currently pursuing BSc Computational Mathematics. I chose this degree because I genuinely love mathematics, but lately I’ve been questioning whether I made the right choice.

Most of my friends are going into MBBS, nursing, dentistry, and other healthcare fields, and seeing their career paths sometimes makes me feel like I’m falling behind.

I chose mathematics because it made me feel alive, but sometimes I wonder whether, in a country like mine, following that love was a luxury I couldn’t afford.

I’m attaching my university’s syllabus. Does Computational Mathematics offer good career and higher-study opportunities internationally, and can it lead to a financially stable career?

I’d especially love to hear from people who studied mathematics or a similar field in a developing/third-world country and managed to build a successful career, but opinions from anyone in the field are welcome.

So I got enrolled in B. Tech in mathematics and computing and I don't have any idea what's this department is and what are my future carrier options.

This course is already running in many IITs and NITs and many passout from this course.

It is very helpful if passout from this course help me know more about this course, provide resources for academic, what are things other than course I need to do to boost my career and give career guidance I am very thankful.

Thank You

Hey,
Ik twijfel tussen de bachelors econometrie aan de uva en applied mathematics aan de tu delft. Ik wil graag wiskunde toepassen, ik heb alleen nog geen idee waarop(daarom houdt econometrie me tegen want ik weet niet of ik het zeker wil toepassen in de economische wereld), ik haat zwaaaaar bewijzen (daarom houdt TU delft applied mathematics me tegen). Ik wil graag een brede bachelor doen zodat ik me nog kan heroriënteren in de toegepaste wiskunde wereld, alleen weet ik zeker dat ik niet zwaar wil bewijzen. Volgens bronnen heb ik vernomen het enige wiskundige veld dat wordt afgenomen voor econometrie studenten de natuurkundige toepassing is en al dat natuurkunde bevat zwaar bewijzen.

Welke studie zou ik nu het beste kunnen doen?
bedankt alvast!!!

Hi, I’m currently doing a CS degree in the UK and I’ve been thinking about applying for master’s programs in applied math, computational/applied math or CSE in the US.
The thing I’m worried about is whether I have enough math for these programs since I’m coming from CS rather than a math degree.

During my degree I’ve done Calculus I & II, linear algebra, probability and statistics, discrete math and set theory. I’ve also studied multivariable calculus and differential equations on my own. I’m currently working on a research project involving a theorem prover where my work is related to number theory.

I’ve read through the admissions requirements for quite a few programs and some mention courses like real analysis or advanced calculus. The problem is that in a UK CS degree we don’t really have the option to just take extra math electives from the math department, so I haven’t been able to take those formally.

I’m wondering how much that actually matters for applied/computational math master’s admissions. Has anyone here gone from CS (or something similar) into an applied math/CSE master’s without having all of those math courses formally on their transcript? What kind of math background did you have when you applied?

Some programs I’m looking at are UW Applied & Computational Math, Maryland AMSC, Johns Hopkins Applied Math & Statistics, UChicago Computational & Applied Math, and Georgia Tech CSE, just to give an idea of the type of programs I mean.

I want to persue ma economics from dsu cause i heard alot about it. I did not have maths in 12 nor in my bcom. Do i qualify for it? If not can i take a prerequisite course from coursera for maths and then qualify?

I'm a junior transfer at NYU this fall as a Math + Computer Science (MCS) joint major, but I'm considering switching to the Computer + Data Science (CDS) joint major.

They both are pretty similar for the first half of classes: Calc I–III, Linear Algebra, Discrete Math, Intro CS, Data Structures, CSO, and Algorithms.

After that, the main difference is:

**MCS has:** Analysis, Algebra, Numerical Computing, Operating Systems, advanced math electives, and CS electives.

**CDS has:** Probability & Statistics, Data Science I & II, Machine Learning, Causal Inference, Data Management, Responsible Data Science, plus CS/DS electives.

Career-wise I'm interested in anything like SWE, quant, data science/engineering, and ML, so I'm trying to figure out which major gives me the best combination of career flexibility and useful coursework. So far I have a finance and technology summer internship with a couple python projects.

Assuming I can finish both in the same time, for people familiar with NYU or these fields, would you stick with Math + CS or switch to Computer + Data Science, and why?

Hi everyone,
I’m a BSc Mathematics student and recently finished a course in PDEs. My professor suggested that I take another course he teaches called Optimal Control. The course is offered by the Engineering faculty, but he mentioned that it has a strong mathematical component.
I’m considering taking it, but I’d like to understand how useful/relevant Optimal Control is from a mathematician’s perspective.
In particular:

Is Optimal Control a good subject for a math student who enjoys applied mathematics, PDEs, optimization, etc.?

Where is optimal control actually used in industry?

What kinds of jobs or fields make use of this knowledge? For example, robotics, aerospace, quantitative finance, autonomous systems, energy, machine learning, etc.
Is it mainly useful for people going into engineering/research, or can it also be valuable for mathematicians going into industry?
I’d especially appreciate hearing from people who studied mathematics and later encountered control theory or optimal control in their work.
Thanks!

What industry roles involve substantial use of combinatorics, discrete mathematics, graph theory and advanced problem-solving?

For those working in such roles:

What are the relevant job titles?

What types of problems do you typically work on?

How much mathematics is involved in your day-to-day work?

How much of the role consists of programming or software engineering?

What skills and educational background are typically required?

What is the day-to-day work experience like?

Which industries or companies tend to have these types of roles?

I'd particularly appreciate responses from people with direct industry experience.

I'm aiming to startup in deep tech AI—specifically building novel AI architectures, spatiotemporal models, and theoretical foundations (e.g., geometric deep learning, graph networks, advanced optimization) rather than just applying pre-built PyTorch wrapper libraries.

Im evaluating whether choosing a BS in Mathematics (eg IIT BOMBAY) gives a genuine mathematical edge for deep tech AI compared to a standard B.Tech CSE / Data Science degree.

​Skill Gap: In real-world deep tech, how difficult is it to bridge the software/GPU engineering gap on your own if your degree focus is primarily theoretical math?

Would appreciate honest perspectives from anyone in BS Math, CSE, or AI research/R&D. Thanks!

Hi, I am a rising junior and I'm looking for a CAPSTONE project to do as part of my college application. My area of interest include Applied Math and Economics. Any suggestions would be great, looking for any type of advice on activities to do outside of this aswell from any alumni or current attending students at the top 20 schools in the US for my areas of interest.

I Have BSc BEd qualifications, I'm planning to pursue MSc degree through Distance mode in KSOU. Which Course has more career opportunities in future MSc physics or MSc mathematics. I'm preparing for govt teaching jobs, and working in pvt school. Please reply.

Hi everyone,

I’m currently pursuing a 3-year B.Sc. in Mathematics from India and planning to apply for MSc programmes in Germany, mainly in Mathematics, Applied/Computational Mathematics, and Mathematical Data Science.

I’m targeting Summer 2028, with Winter 2028 as a backup.

I’m confused about the ECTS requirements for German universities. My 3-year degree is equivalent to around 144 ECTS, but some universities require 180 ECTS overall, while others specify requirements like 100–120 ECTS in Mathematics.

For anyone who has applied to German universities with a 3-year Indian B.Sc. Mathematics: how were your ECTS and subject-specific credits evaluated? Were you able to apply despite having fewer than 180 ECTS?

Any personal experience or advice would be really helpful. Thanks!

my_qualifications

Is anyone here involved in mathematical biology research? I am looking to learn more about this area. If you are involved in mathematical biology, I would appreciate any advice or discussion about current research problems.

Here's an article applying tropical algebraic geometry to neural networks. I'm organizing a reading group on this topic, please let me know if you're interested. Thanks.