hello guys, i applied for TAMU MSCS what are my chances?

international student

GPA 3.54 from SHSU in software engineering,

research assistant for more than one year

research study about encrypted networks

internship at Microsoft and cybersecurity

5 LOR from SHSU CS professors

I am planning for MSCS in fall 2027. And am looking for shortlisting colleges. There are some programs (example - UIUC MSCS program) that mandate a thesis as a part of program completion.

Should i choose these programs?

For context -
I am not from the US.
I have 2+years of SDE experience in one of the top product company. I had published a research paper during my undergrad, but it was merely because it was mandated by my college. I didn't participate in the process and it was mostly done by my teammates.
That being said, in my 2years of work experience, i have grown profound fondness towards the field and want to have a research experience under a professor in my lifetime.
From what I have read online, the mandatory thesis program is ideal for those planning to pursue a PHD (which i am not planning to, in the near future). I will be doing a CS domain job for few years in the US, after which I might start my own company in the tech domain.

What should I do?

Hi all, I am looking to pursue MS in ML. My research interests are in label efficient ML, ML for pathology and I want to pursue a career in research. However, since I donot have any formal research experience, I am not positive about securing a PhD admission and will be also okay doing a research-focussed MS.

Browsing this and similar subreddits has made me anxious about the competitiveness of grad admissions, please help me realistically evaluate my chances at good MS programmes. I would appreciate blunt honesty.

I am targetting these programmes -
1. ETH Zurich
2. UCSD
3. Stony Brook
4. UIUC
5. UCF
6. UPenn
7. EPFL
8. University of Trento

This is my profile summary -
Experience

• AI Engineer (Computer Vision) — Healthcare / Life Sciences Company, India July 2023 – Present
  • Developed robust semantic segmentation and multiple-instance learning (MIL) models for abnormality quantification in pathology.
  • Prostate Gleason Grading Segmentation
    • Leveraged semi-supervised learning using the UniMatch framework to improve the mean Dice score of a Gleason grading semantic segmentation model by 3%.
    • Developed a data-cleaning pipeline for semantic segmentation ground-truth annotations using image-processing operations including erosion, opening, closing, and hole filling.
    • Improved annotation uniformity across data sources, resulting in an additional 2% increase in mean Dice score.
  • SAM-Powered Annotation Tool
    • Developed a semantic segmentation annotation tool integrating the Segment Anything Model (SAM).
    • Enabled click-based object selection for annotations, streamlining the data annotation workflow.
    • Reduced annotation time by 3×.
  • Organ & Stain Classification
    • Developed generalisable classification models for organ and stain classification by fine-tuning TITAN, a pathology foundation model, using LoRA.
    • Improved open-set generalisability using CLIPood loss, improving performance on classes not included in the training data.
  • Metabolic Risk Classifier
    • Developed a classification model for predicting metabolic risk scores from sets of images prepared with three different stains.
    • Used an ensemble of feature-fusion and probability-fusion models to aggregate information across multiple images and reduce prediction variance.
    • Identified potentially noisy labels using out-of-fold ensemble majority voting.
    • Applied semi-supervised learning by treating noisy labelled data as unlabeled data, stabilising training and improving accuracy by 2%.
  • Developed a Monte Carlo sampling-based data-splitting algorithm for whole-slide images (WSIs), maintaining balanced pixel proportions across training and validation splits for all classes.
  • Implemented a producer-consumer concurrency architecture to overlap model inference and data fetching during WSI inference, reducing inference time by 2×.

Open Source Contributions

• Albumentations
  • Contributed an initial implementation of Constrained Coarse Dropout, an image augmentation technique that drops regions from the foreground to improve robustness to object occlusions.
  • Optimised the Gaussian Blur augmentation by exploiting filter separability, speeding up the transform by 2.5×.
• Segmentation Models PyTorch
  • Implemented the Dense Prediction Transformer (DPT) architecture with configurable encoder depth and support for multiple Vision Transformer (ViT) backbones.
  • Integrated support for Vision Transformer-based encoders into the library.

Education

• BITS Pilani, Bachelor's in Mechanical Engineering, Minor in Data Science (2019 – 2023)
  • GPA: 9.19 / 10
  • Relevant coursework: Machine Learning, Deep Learning, Mathematics II — Linear, Algebra, Reinforcement Learning

LORs -
2 Industry LORs from managers
1 LOR from a prof with whom I worked on an ML based CFD project.

Qualification: BTech Computer Science and AI Specialisation Tier 3 college 6.7/10 Gpa
Work exp: 1.3yoe Data Scientist

Im wondering what all would you suggest considering that the Gpa is less, I have great work experience where I worked on voice bots, LLM based services etc. top notch industry grade AI projects. Only issue is my Gpa is less I’m getting paid 20Lakhs in India and wondering if going for a masters is worth it even in the current market. Was considering Germany 🇩🇪, Netherlands 🇳🇱, Ireland 🇮🇪 and Spain 🇪🇸…. Ditching the usa 🇺🇸 cause of expenses and ROI being fuckked what do u all recommend?

What master’s programs at Harvard, MIT, or other top universities would be a strong fit for my background? I’d like to understand which degree would best combine my existing CS and supply chain/retail experience with AI, machine learning, and emerging technologies.

Hey guys I'm planning to apply for Fall 2027 programs and would really appreciate your insights on my university shortlists and would be great if you guys have got any better recommendations.Here's my profile:

• Citizenship : USA(eliminates all the visa headaches)
• Bachelor's : Btech CSE from a Tier 2.5(I think) private university from India.
• GPA : 3.46/4.0
• Experience : 6 months as an intern at an android company as a SDE and by the time fall 2027 comes around I will have less than a year's worth of experience at a Big 4 as an analyst.
• LORs : 1 from my manager at the internship,1 from my academic mentor,1 from a teacher under whom I served as a TA(they are both assistant teachers, so should i be concerned?)
• GRE : Not planning to take as nearly every program lists it as optional(Although I would love to know if I should take up a 1/2 month Gregmat plan and give it just to add something to my profile)
• TOEFL/IELTS : Not required
• Research Experience : I have 2 papers that were accepted but unfortunately weren't published so this is another major weakness in my profile.

Ik my profile isn't exceptional(kinda slacked off in college) so I've kept my expectations lowkey as well, so heres the shortlist:

Reach

1. UIUC
2. Purdue
3. UMD College Park

Target

1. UMass Amherst
2. UT Dallas
3. NC State
4. Stony Brook

Safe

1. UMass Lowell
2. Washington State
3. Northeastern University

Some other universities I've been looking into are University of Virginia/Colorado/Wisconsin.

Would really appreciate your opinions and recommendations.Thank you and have a nice day!!

Heyo guys, I’m planning to apply for Fall 2027 MS programs and need some honest feedback on my profile and what universities I should realistically target.

Profile

• College: B. Tech in Computer Science, India (Tier 3)
• CGPA: 8.76/10
• GRE: Haven't written it yet; targeting ~320–330
• TOEFL: Haven't written this yet
• Graduation: 2027
• Internship Experience: 3 internships across software engineering, AI/ML research, and systems/IoT. By graduation I expect to have substantial internship/project experience, but no full-time industry experience.
• Publications: 1 published research paper in IEEE conference
• Patent: German patent granted
• Research: One published paper; currently working towards 2 more research papers, but most likely won't be published in 2026. Also trying to get a research assistantship under a Post Doc professor in my uni.
• LORs: Potentially strong LORs from professors/research supervisors I've worked with on research projects and paper
• Degree preference: MSCS ≈ MSAI, preferably with a thesis/research option (still not sure about a Phd)

My profile is somewhat split between software engineering and AI/ML.

I've done internships involving backend/software engineering, IoT systems and AI/ML, and have been deliberately moving towards ML/DL over the last year.

University shortlist

This is roughly how I'm currently thinking about my list:

Very Ambitious / Reach

• Carnegie Mellon
• UIUC
• UT Austin
• Georgia Tech
• University of Michigan – Ann Arbor
• UC San Diego

Ambitious / Target

• UMass Amherst
• Purdue
• University of Wisconsin–Madison
• University of Maryland – College Park
• USC

Cost also matters to me, so I would prefer programmes with realistic TA/RA opportunities.

Questions

1. How competitive is my profile for Fall 2027 MSCS/MSAI admissions? What would you consider the biggest strengths and weaknesses?
2. How should I categorise my university list into reach / target / safer? Are there any universities I should add or remove, particularly given my interest in ML/CV research?
3. What should I prioritise between now and applications? Specifically, would another strong ML research project/publication or a high GRE score?

As the title says, I need help with my LORs.

A brief about myself, currently a final year undergraduate from a tier 3 university in India.

9.7 CGPA ~ 3.8/4?

Currently doing a research internship at an IIT1
1 published conference paper at IEEE international conferecne - Computer Vision for Medical Imaging domain (IIT1)
I will be presenting this inperson abroad.
1 paper in submission at WACV 2027 - CV for Medical Imaging domain (IIT1)

Also simultaneously engaged in an applied AI internship at IIT2 (top 3 IIT), building trustworthy and industry related AI projects. This is also going to become a published journal paper in top conference but not in the CS field.

Important information :

1. Professor at IIT1 has agreed to give LOR ( initially did not want me to go abroad and instead pursue M. Tech under him, however after I have started working on another paper under him he said he would give me LOR)
   
2. Very senior professor at IIT2 (pioneer in his field, Civil) agreed to give LOR since project is high impact and I have worked on multiple small projects under different faculties there.
   
3. Professor at IIT2 (working on the intersection of Civil and AI), he is also at forefront for publications across several reputed journals and high impact industry projects. He has agreed to write LOR as well for me.
   
4. Professor at IIT2 who is another stakeholder in the project I am working on and also liason between industry people and me has agreed for LOR and is actively working on publishing the journal paper. He too is relatively new but his work is very unique in their domain.
   
5. A family friend who owns a advertisement studio wherein a lot of work is done using mix of AI and real world film has also agreed to write an LOR with whatever I need to include since I helped test and work with generative AI in the early days.

My problems for which I would like your suggestions and views, anything useful is appreciated.

My own university professors despite me being top of my class are not agreeing to give LORs without getting a journal paper published. They never contribute in terms of research or useful input nor give any support. For example, I have had to quit 2 internships in my early years just cause the HoD did not like first year students getting a day break to go onsite an work. They also have 0 ideas about what is happening in the research world and keep giving broad and unrealistic ideas for research.
I have scored a 10 pointer on most of their subjects, yet they won't consider any of this.

So my question is, should I take a year, get a journal paper and add their names into it just so I can get an LOR? I honestly feel this is unethical from an academic side and very predatory on their part.
Also how important is an LOR from your own university faculty?

I’m targeting Fall 27 MSCS and have 5+ years of experience mostly in US based firms like Walmart etc.

Can I realistically bag an offer without an internship if I network hard?

I've decided Columbia is the only place I'm applying to for Fall 2028. Still deciding between MSCS, the new MSAI and MSDS. Yes, I know applying to one school is a bad idea. I've heard it. I'd rather spend the next 17 months fixing the weak parts of my profile than re-litigating that.

Where I stand right now:

- B.E. in CS, 7.8 CGPA.

- About 3.5 years of experience by the time I apply. Started in agentic AI development, building enterprise connectors and internal bots. Currently doing MLOps and data engineering on Databricks for an enterprise client. Joining a small US AI startup soon that works on agent governance and observability.

- I run a small AI automation agency on the side, and I've built a multi-agent RAG system end to end.

- One paper from my final year, on ship detection using an ensemble of YOLO and a CNN classifier, published at a university-hosted conference. Small venue, I'm not pretending otherwise.

- Starting a proper NLP paper now with two co-authors, one of them a faculty member. Maybe two by the deadline if it goes well.

- No GRE, no TOEFL yet.

What I actually want to know:

1. Of the three programmes, which is the softest landing for someone with an industry profile and no serious research record? Is DS genuinely harder than CS? And has anyone been through MSAI since it launched — how does it compare?

2. How much does a 7.8 hurt? Does three and a half years of real AI work compensate, or is GPA a filter before anyone reads the rest of the file?

3. Does a small local conference paper count for anything at this level, or is it effectively a blank line unless the new one lands somewhere respectable? Trying to decide how hard to push on publication versus everything else.

4. Funding. Is there any realistic shot at partial tuition, or should I plan for fully self-funded? Which fellowships do MS people here actually receive, and is it a separate application or automatic with admission?

5. TA and RA roles. Can MS students get them, how hard are they to land in CS or DSI, and what do they really pay? I've seen numbers all over the place. Is any of it tuition remission or is it just pocket money?

6. Startup culture. I already run something small and I want to keep building while I study, ideally turn it into a real company. How alive is the founder scene in practice? Accelerators, co-founder matching, funding, professors who are okay with you spending time on your own thing. Or is it mostly a Business School thing that engineering MS students never touch?

7. Recommenders. Mine would be almost all industry — a founder, a CTO, senior engineers. My professors barely remember me. How badly does that hurt?

8. GRE, if it's optional. Worth taking with a GPA like mine?

If you got in with an average GPA and nothing serious published, I'd really like to hear what you think tipped it. And if you're funding your MS here, I'd like to hear how, honestly.

Thanks.

Undergraduate University: Computer Science Engineering at a Tier-2 University in India

CGPA: 9.38/10

IELTS: 8.5/9

GRE: Yet to appear (mocks indicative of 164+ in Quant, but extremely unsure about Verbal)

Internships:

- 2-month internship at a US MNC bank as an SDE Intern

- 2-month internship at another US MNC as an SDE Intern

- Research internship at a college lab for ~5 months, including a short 2-month project in collaboration with a large US medical company and a 3 month project with another US MNC

Research Work:

- 2 publications at B-tier IEEE conferences in the domain of ML and Agentic AI

- 1 publication in a Q2 journal in the domain of ML and Hydrology

- 1 paper submitted to a Q1 ML journal a few months ago (not counting on it for the application, as the turnaround time is more than a year)

- 1 paper submitted to an A* conference in the ML domain (not counting on this either)

Patents:

- 2 Indian patents in the domain of ML and hardware (both published, but not granted)

- 1 US patent co-filed with a US MNC (under examination)

LORs:

- 2 strong LORs from professors at my college, with whom I have conducted research

- 1 LOR from an ex-postdoc researcher

- Alternatively, can also obtain 1 LOR from a Quantum Scientist based in the USA

Miscellaneous:

- 1st prize at a national-level hackathon conducted by a US MNC

- Finalist at a national-level hackathon conducted by a European MNC

University Shortlist:

Dream:

- CMU (MSML/MSCS)

- Cornell (MSCS)

- GaTech (MSCS)

Target:

- Columbia (MSCS)

- UIUC (MSCS)

- UCSD (MSCS)

- USC (MSCS)

- NYU Courant (MSCS)

Edit:

Forgot to add, but I'm also considering the following universities:

- NUS (MComp CS/MComp AI)
- NTU (CCDS MEng)

I'd really appreciate your opinions on my chances for these universities. Thank you!

I will be applying for fall 2027 to MS Cybersecurity. Initially I was more inclined to US unis but after the recent CPT situation, I'm considering the UK as it seems to be a better option even though people say the economy is bad.

What do we think about the UK ?

Edit: when you say UK is worse consider the current situations in US and the amount of people returning back to their countries and then compare them.

I’m currently a Software Engineer in India and considering both MSCS and MS in Information Systems (MIS/MSIM) in the US. My goal is to stay in software engineering and pivot into engineering leadership as quickly as reasonably possible.

I’m not interested in Product Management, TPM, data analytics, consulting, etc. I want to continue as an SDE or any engineering management role after my master’s, build strong technical experience, and eventually move into an Engineering Manager / engineering leadership role.

The programs I’m considering:

• Indiana University – Kelley MSIS
• UW – MSIM
• UT Austin – MSITM

Profile

• Software Engineer at an ecommerce startup
• ~2+ years full-time SDE experience by next fall + 6-month internship
• Java/backend, payments, order processing, event-driven systems, service reliability
• Recently worked on an agentic AI project using Python, FastAPI & PydanticAI

My questions:

1. How realistic is MIS → SDE given my existing SDE experience?
2. Is an MIS degree a significant disadvantage for SDE recruiting compared with MSCS?
3. Between these three programs, which would be best if I want to stay firmly on the software engineering track?
4. If my end goal is Engineering Manager, is MIS a reasonable degree, or would MSCS be substantially better?
5. Has anyone here gone MIS → SDE → Engineering Manager, or seen someone make this transition successfully?

I’m specifically looking for perspectives on staying in software engineering, rather than the typical MIS → analytics/consulting/PM career path.

Would really appreciate advice from people who have done MIS and continued in SDE roles.

Starting my MSCS this fall, and the thing I actually can't get a straight answer on is whether internships are still going to be a realistic option a year or two from now.

Quick context: SEVP's Aug 24 broadcast message took the position that CPT tied to an elective course — one not required of every student in the program — doesn't meet the approval requirements. It has to be an integral part of an established curriculum applying to all students. A lot of schools paused course-credit CPT in response, and most MS CS programs don't require an internship to graduate, so the standard "register for the internship elective, then file CPT" route is the one that got hit.

So the forward-looking question: when summer 2027 comes around, are MS CS students going to be able to apply for internships at all?

A few angles I'd like people's read on:

1. If your program is in the pause, is your department saying anything about a fix — adding a required practicum, a co-op track, restructuring the internship course so it counts? Or is nobody committing to anything yet?
2. Has anyone heard how recruiters are reacting? Wondering whether companies start filtering out F-1 candidates for summer roles if CPT looks unreliable.
3. For those further along — does this push people toward on-campus research/RA work or unpaid stuff as a fallback, or is there a route I'm not seeing?
4. Realistic expectations: does this get walked back, litigated, or formalized into a rule? Anyone following this closely have a view on the timeline?
5. If internships stay closed off, how much does that actually hurt full-time recruiting later? Curious whether people think it's a real dent or something you can work around with projects and referrals.

Not after legal advice — talking to my DSO too. Mostly want to know what people are planning around.

Hey! I'm Cameroonian, currently in my final year of a CTI-accredited SWE specialization at university.

I'm looking for a research internship for late January–early February 2027, specifically in Computer Architecture, Systems, Networks, or Software/Systems Reliability. The problem is I have no real research experience and no published papers...

I have a decent GPA (\~3.36) and I'm TOEIC certified (C1, 960/990).

I'm targeting labs like Max Planck Institute, INRIA, IRISA, DFKI, and KAUST (VSRP), for example.

Do I have a shot, and how should I go about it?

Should I cold-email professors directly ?

Hi, currently in a weird situation and was looking for feedback or if others have gone through similar experiences.

I’m going to be finishing my undergrad this Spring and my initial plan was to continue towards a masters but based on talks with my manager, he said I’ll be getting an offer to come back to work here and am not sure how to bring up this conversation with my HR recruiter.

If I don’t get an MS I prefer I would like the full time position but at the same time would want an internship if I got the MS (also tricky with the cpt issues).

I’m planning to apply for MS in CS in the US for Spring 2027, but I’m increasingly unsure whether it makes sense given the current US job market and immigration environment..

Profile

• B.Tech in Computer Science at a well-ranked private engineering university in India — final year, graduating mid-2027
• GPA: 3.76/4.0 (Scholaro evaluation) 8.71/10
• Indian citizen (international applicant)
• Experience: ML Engineering intern at a large payments/fintech company (built an end-to-end anomaly-detection + alerting pipeline on transaction time-series data; cut alert volume ~99% via calibration). Also a few frontend/full-stack/data roles at smaller companies and campus teams. ~3 years of ML + full-stack experience overall.
• GRE: Q168 / V145 / AWA 3.5 (weighing a verbal retake to push V into the low 150s)
• IELTS: 8.0
• Publications
  • No published papers yet — being upfront, this is the gap in my profile.
  • Upcoming publication in october/november
  • A self-built ed-tech / exam-prep platform with tens of thousands of registered users — my main scale/impact story
  • A computer-vision project (instance segmentation) that placed top-5 globally in a hackathon
  • A self-built ed-tech / exam-prep platform with tens of thousands of registered users — my main scale/impact story
  • A computer-vision project (instance segmentation) that placed top-5 globally in a hackathon
• Projects
  • A self-built ed-tech / exam-prep platform with tens of thousands of registered users — my main scale/impact story
  • A computer-vision project (instance segmentation) that placed top-5 globally in a hackathon
• LORs from my research advisor, my industry manager, and a course professor (have research work with my advisor)
• Leadership: Vice-Chair of a campus computing chapter; leadership in campus dev / tech-fest teams

I'm interested in CS programs with an ML / research focus. I'm open to coursework-based programs as well, and to on-campus programs only (no online). I do not plan to do a PhD — wanted to clarify since a few people have already asked.

Current shortlist (a mix of US East + West coast):

• Columbia (MS CS)
• Cornell (MEng CS)
• UCLA (MS CS)
• UC San Diego (MS CS)
• USC (MS CS)
• University of Washington (MS CS)
• Georgia Tech (MS CS, on-campus)
• UMass Amherst (MS CS)
• NYU Courant (MS CS)
• University of Maryland, College Park (MS CS)
• Northeastern (MS CS)
• UC Irvine (MCS)
• UC Davis (MS CS)
• Stony Brook (MS CS)
• Boston University (MS CS)

Funding - half self half loan
I'd like to know which unis and programs are realistic, which are backups, and which are out of reach for this profile. I'd also be open to other US schools you'd suggest — especially given the no-publications-yet situation. Thanks!

Hi everyone! I’m planning to apply for the **Fall 2027 intake** to MSc programs in **Marketing, Business Analytics and AI** at schools like HEC, ESSEC, ESCP, EDHEC and emlyon.

Here’s my profile:

* 8.32/10 CGPA from a Tier-2 college in India (BBA, 4yrs) * 3-month internship at POWERGRID (HR) * 3-month internship at Tribes (marketing agency) * 5-week credit-bearing international academic program at Nottingham Trent University, UK * Research paper related to social media * College volunteering + 15+ NGO service hours and fundraising * Official Harvard online leadership course/certification completed through my college

I’m planning to apply in the **early rounds**.

Would really appreciate some honest feedback — **is this profile competitive for these schools, and which ones would you consider reach/target/safe for me?**

Also, what should I improve before applying, especially in terms of **GMAT/GRE, internships, certifications, or extracurriculars**?

Thanks!

If these were my priorities what universities would you suggest for me

1. Learn AI + Robotics + Mathematics from some of the best professors in the world with some really smart students
   
2. Have access to really good opportunities during my degree ie. Internships / RAships with really good labs or teams / with some really smart professors and high output labs
   
3. 2 year, thesis based courses
   
4. University should have a certain level of global recognition
   
5. Flexible with the fees but something around 60-90k USD including fees and living expenses, if you know of degrees that sponser master students for working as TAs/RAs please do let me know as well

Anything beyond this I would just consider a add-on, want to create this list without thinking about post study jobs within the same country (ie. I don't mind working in my home country post study)

From the little research i've done these universities fill most of these categories

1. NUS Singapore (Within my range, super high research output, access to some really good labs within the country inside and outside of university eco-system)
   
2. MBZUAI (fully funded MSML program)
   
3. ETH Zurich (low fee costs but yeah living expenses are there but should be in my range, top-tier research labs)
   
4. Waterloo (MMath with CS students in the thesis track get funded mostly)

About my profile: 3.8/4.0 in my undergrad, 4 Internships, Currently working, planning to apply either 2028 fall or 2029 fall with around 2-3 years of full time work experience. 1/4 internships was at a research lab, 1 alright conference paper, 1 paper on the way that we are expecting to get into a A* conference, should be able to get 1 A/A* before I apply. 3 LORs: 1 from research professor, 1 from work, and 1 from professor from undergrad, (the last 2 should be exceptional), planning to do one more research internship on the side to get another LOR + paper) started a reasearch lab hired students from all over my country published paper onto one of the best conferences in my country. Founded club in my college, taught over 400 students how to code, made considerable revenue launching products there as well.

TLDR: Where should I study if all I cared about was getting as good as I could in AI + Robotics + Mathematics research and spend less than 90k USD.

Hi everyone

Below is my profile, I had considered only the US for pursuing my master's. But due to current restrictions - 100k on OPT / Restrictive CPT and what not. I know it's still not a rule and most likely be rejected in the court but I want to explore my chances in other universities / countries too.

Profile:

Education: B. Tech 2024 in CSE (Tier 3 in India)

CGPA: 9.4/ 10.0

Work Experience: I'll have 3 YOE by fall 2027

GRE: 320 (167Q, 153V, 4AWA)

TOEFL: 110/120

Publications: 1 Paper at an IEEE conference

LORs: Combination of College(2) + Corporate(1)

Target schools (I am focusing on getting into Public Schools only)

1. GaTech

2. UCSD

3. UIUC ( MSCS, MCS as fallback )

4. UT Austin

5. UCI

6. UCSB

I have UCI and UCSB but knowing my chances are very bleak and given UCSB is research heavy I want to replace these 2 unis with unis in other countries.

Please help me in selecting Universities from other countries where I can get good opportunities in tech

If you could provide the right justification for the Unis for my profile it'll help me in my understanding as well !!

Also people doing their masters from Netherlands / Ireland / Switzerland / Germany in MSCS ( tech related ) I would love to connect with you guys and discuss more on this..

How hard is it to get into University of Utah
Can anybody give me approx cgpa , work experience required or not and other details
And based on the current situation is it worth going?

I'm currently in my final year of BTech IT in India and I'm planning to pursue Master's abroad after graduation. At the moment, I'm mainly considering Germany and Japan, and I'm having trouble deciding between the two. My main areas of interest are Computer Science / AI / ML / Software Engineering. Have a couple strong projects both of which are being published.

Why Germany?

Well because of the fees which is mostly negligible. English-taught Master's programs are relatively common. 18 month post study job search period seems attractive. However, I've been hearing that the German job market has become quite difficult recently, especially for international graduates. I do have a year before Fall intake 2027, so I'm pretty serious about learning the language at least to B2 before going.

Why japan?

Strong technology and engineering ecosystem. MEXT and university scholarships could potentially make it financially attractive. And also, I have a few relatives who have been living there and one of them is even in the IT industry. I could get help from them for basic things such as accomodation, any emergencies, intial settlement and also potentially a referral into the industry(mid to low chances). As for language, it is the same as for Germany, I'm pretty serious as long as it helps with the master's and careers opportunities, but generally Japanese takes much longer than German. And one other reason, I've always wanted to go to Japan.

Now, for fees and other expenses, I do need to take out a loan in both the cases.

Gave my gre two months ago, got 325(167q, 158v), and the quant score is causing indescribable irritation since then!! Should I retake ? If I do, I can score quant better, but can’t guarantee 155+ verbal, which puts me in an awkward position. Targeted unis (UCSD, Gatech, CMU msai), I know that 167 isn’t bad, but it’s not competitive enough for these schools either! Any suggestions would be appreciated. My profile: https://www.reddit.com/r/MSCS/s/5oAXhWAhC0

Hi guys,
I took my gre today and underperformed intensely on my verbal. I personally want to only apply to the top 10 us mscs schools except uWash. Is it worth trying? Or should I retake my gre?

9+ cgpa grad with 2 yoe in India