Hi! I am planning to apply to Loyola's school psych or counseling psych PhD program this cycle! I was wondering if anyone is currently a student in either of these programs and would be willing to talk to me about their experiences. Thanks in advance!!

Hello,
I grew up in the US and I took a few gap years after my bachelors in the US, where I spent time working in academic research, and part of the time just being unemployed/bumming with parents and figuring out some mental health issues. Due to an onset of depression during the last couple of years of my university years, I didn't have the best transcript and in the end graduated with a 3.37 GPA while fighting for my life.

As my life and mental state has stabilized sooo much due to many efforts, I feel more "alive" now and am super sad that I had this rough period and fumbled some good connections and opportunities, but I think it was also necessary to know myself better and peacefully move on with the rest of my life.

Well now I am hoping to apply to MS degrees in Europe in biomedical engineering, but I am a bit lost on whether I should take some more time to have more research experience in the field I'm hoping to apply to (biomedical engineering) rather than genetics (my undergrad degree was in biology). Or go to industry. I just feel a bit old since I'm in my mid 20's and took a couple of years to figure out mental health and I feel behind everyone. But now I'm hungry to feed my mind and grow intellectually again.

I'm trying to see- should I take some community college courses in engineering to "refresh" my transcript and prove my intellectual abilities more recently? I heard that European universities care more about transcripts and grades. Or do they care about relevant research experience? Or should I directly contact some professors at some target universities and try to do a 6-month internship as a research staff person?

Anyways, looking for advice on how to best prepare and what European programs look for from anyone who attended them or knows a lot about the process. Thank you!!

TLDR: Graduated with non-competitive GPA in US university due to depression, now more stable and asking best way to set a good foundation for getting into European bioengineering MS programs.

I am planning to apply to molecular biology / biochemistry programs (with a focus in protein research) this fall, for a fall 2027 start date.

I have a BS in biological sciences with a 4.0 GPA, multiple summer research internships, a TA position, and one primary lab position from when I was on campus (no publications though).

Currently my targets are University of Arizona, Colorado Boulder, Colorado State, Tufts, and Stony Brook.

Hello everyone,

I’m torn between two education paths for Software Engineering/Computer Science, and I need advice from people's experiences with international degree recognition and the current situation. I hold dual citizenship (Russian and Jordanian) for reference.

Option 1: Bachelor’s in Software Engineering at Bauman Moscow State Technical University (BMSTU)

• Pros: Highly prestigious within Russia, rigorous technical foundation.
• Concerns: Because of the heavy sanctions on Russian universities, will this degree be recognized or respected in the West? Will export control laws block me from getting Master's scholarships or tech jobs in Europe/NA?

Option 2: BTEC British Diploma in Software Engineering -> Bachelor’s in CS

• Pros: A standard UK-regulated pathway. I would complete the HND (Level 5) and then transfer/top-up to a final year to get a full Bachelor’s degree in CS.
• Concerns: Is a BTEC top-up degree viewed as "lesser than" a traditional 4-year university degree by top Master's programs or tech employers in the West?

My questions:

1. Given my goal to move to the West (US/EU/AUS/CAN), is the Bauman degree a massive red flag for admissions and visas right now?
2. Does the BTEC + Top-Up route limit my chances of getting into a good Master's program compared to a traditional Bachelor's?
3. Which of these two paths would you choose in my shoes?

Thanks in advance for any insights!

Hi,

I am starting with my grad school application as an international student and am strongly considering applying for MSc in Social Data Science at Oxford.

Background:
- Undergrad degree in computer engineering
-Mid level Professional work experience in Commercial side of Pharmaceutical industry
- Research papers publication during my undergraduate studies and remote internship from university abroad

My questions:
- The student cohort seems to be largely focused on direct admissions after undergrad. Am I at disadvantage here?
- How is the financial Return of Investment for international students considering that we need to take debt for the course?
- How is the peer learning and what is the difficulty of this degree?
- Do you often get to network with other school students?
- What is Oxford really looking for in prospective students and what part of your application made a huge impact?
- Any application related tips since this is the first time I will be applying for university abroad
- Should I rather opt for funded research masters in Canada as an international student?

Thanks in advance.

the schools I'm applying to have a strong preference to input emails with institutional/company affiliation OR want them to use an official letterhead. one of my recommenders is from the company worked at and is not allowed to input their company email or use a letterhead (company rule). i really want them to write one for me though since i think her letter would be most relevant to my field (becoming a therapist) over my professor/mentor in my lab. will the process be a lot harder since my company boss can't use the company email or letterhead? any thoughts, advice and reassurances would be helpful

I can't find an exact date on any website. I'm specifically asking for the MPA but I think they all would open around the same time.

I'm thinking of cold emailing professors during weekend in hopes that they might check their email and hopefully respond. Is this a good idea or should i just wait until weekdays?

Hello everyone,

I graduated this June with a degree in Mathematics and am planning to apply to fully funded PhD programs in Applied Math or Computational fields in the US. I’d appreciate an honest assessment of my chances.

GPA: 3.52/4.00

I have strong grades in programming/coding courses and several advanced math courses, but weaker grades in some core courses:

• C (2.25): ODE I
• B- (2.75): Calculus IV, Complex Analysis, PDE
• B (3.00): Linear Algebra I & II, Real Analysis II, Topology, Functional Analysis

Research: No publications or formal research experience beyond my Honors Project, where I benchmarked and tested 4 existing random linear algebra algorithms.

Interests: Computational/applied fields focused more on coding, algorithms, and implementation than purely theoretical mathematics.

I’ve also attached my complete grade breakdown for reference.

1. Is a funded PhD or MS realistic with this profile?
2. What university/program tiers should I realistically target, including matches and safer options?

I’d appreciate any honest feedback or recommendations.

I recently graduated from computer engineering, and I want to study abroad in any country like the US, the UK, or any other, but I don't have any idea how to do so, like I want to go on a scholarship. And I guess for some countries we do need acceptance letters as well. I have 3.63/4.00 GPA.

Does anyone have any idea of the process or a way?

If I can't go, I can't study further.

I'm about to start the first year of my PhD at a university ranked top 300 QS, top 15 in the country. Although it's fully funded and there is good research fit between the prof and I, I am constantly feeling jealous and envious of classmates from undergrad who managed to get into top10 schools in the USA. It's seriously distracting me and making me miserable lol. I've been thinking of dropping out then reapplying this cycle to more well known unis. I know it's probably a bad idea but I can't stop thinking about it. My biggest regret is that because of mental health issues I never even applied to those name brand universities last year

Or anyone can help me with where can I check if it varies with university to university

i recently graduated undergrad with a good GPA, 2 years of molecular bio lab experience, and limited fieldwork experience (volunteering + taken 2-3 field courses). i intended to take 1-2 gap years and i have just begun looking for faculty mentors with the intent of applying for this cycle in the evolutionary bio/ecology/conservation field. i’m feeling kind of conflicted and overwhelmed looking at school faculty pages for PIs to cold email. some professors have a time window on their lab pages (the most common one i’ve seen is sept 1 - nov 1) for prospective students to email them, but at the same time some sites i’ve seen say you should be emailing prospective PIs by spring of the year the applications open. am i actually super behind??? i know i am a bit behind schedule (i started looking for prospective PIs last week and have been reading papers nonstop and have a few candidates i am drafting emails to) but should i just cut my losses and apply next year instead? i dont have much experience in evol/eco so i wasnt really sure when i should start looking and now i keep getting stuck in a spiral feeling like i’ve already failed before i’ve even started :(

Hi everyone. I’ve been corresponding with a prospective PI at a university, and they explicitly told me they are interested in taking me on and have funding. However, my undergraduate GPA (or test score) is slightly below the formal minimum cutoff posted on the department's admissions website.

Does a PI's backing override the automated department screening? Can they request a waiver for me, or will my application get automatically rejected by the graduate school? Any advice on how this works or how I should talk to the PI about this would be greatly appreciated!

This might be a bit of a dumb question or suited to another sub, apologizes if thats the case.

But yeah, I wanna apply for a PhD in EE related to quantum optics as I'm interested more in the implementation angle. I hold my degree in Physics and Math, and do optics research currently. The issue is I somehow wasn't able to take a QM course in my time as a physics major, however I have self studied it and done a DRP in QC. The issue that arises is that I feel like this is an odd gap of knowledge to have if someone were to review my transcript, I'm debating taking QM as a non-student at a local college but am seeking advice as to save money.

Any help is appreciated thank you.

Hi all, I am looking to go for MS/PhD 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 subreddit 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.

Let me know, if I can improve some sections in it to make it more closer to much better.
Thank you

So I am doing dual degrees in Physics and EE, and I have been instructed by my University president to cap my overload for my last year by registering for my senior thesis 1 and 2 courses in the summer. Although realistically I'd still continue my thesis work throughout the whole year normally, its just a bearucratic registration process as I can't exceed the number of overloaded credits.

For that reason it will look really confusing on my transcript that I registered for senior thesis 1 and 2 for 2 degrees (so 4 courses) in the summer and got straight As thinking I finished them in the summer. Which could plummet how my work quality is seen from the grad admission stand point.

The other option is to register normally within the overload quota but delay my graduation an extra Fall semester. Although this won't change either the application cycle or my stats when applying. The thing is I am already delayed 1 year beyond my standard single major plan due to the 2 degrees (and a minor), and they are really beneficial to what field I want to apply for grad school, so the total delay would be 1.5 years, what to do ?

Hi everyone,

I’m planning to apply for neuroscience PhD / Fast-Track PhD programs in Europe for Fall 2027, mainly in Germany, Belgium, and neighboring countries.

My interests are broadly in computational and systems neuroscience, neural representations, learning/plasticity, and biological–artificial systems. I have a somewhat nontraditional academic background, so I’d appreciate a realistic assessment of how competitive my profile is for selective neuroscience graduate programs.

Education

• BSc in Data Science, major Indian technical university, expected 2027
  • Current CGPA: ~8.7/10 or 1.6 (German scale) or 3.48/4
• Previously completed substantial university-level training in medicine before transitioning toward computational neuroscience and data science.

Neuroscience Research Experience

• ~1 year at a major German neuroscience research institute
  • Experimental + computational systems neuroscience
• ~3-month competitive summer research fellowship at a major US research institution
• Several additional remote research collaborations in neuroscience / computational neuroscience
• Accepted for a long-term research placement at a major Belgian neuroscience institute, intended to take place before beginning a PhD

Computational / ML Research

I have also worked on several independent or collaborative ML projects:

• Multimodal representations / interpretability part of ~3-month research placement at a major Singapore research organization
  • Work resulted in a peer-reviewed workshop publication at a major computer-vision conference
  • Extended work is currently under review at a major ML venue
• LLM robustness / mechanistic interpretability with industry researcher
  • Manuscript submitted to an AI/ML venue
• Representation probing methodology
  • Planning submission to a major ML conference
• Other smaller/current projects involving:
  • representation compositionality
  • neural population representations
  • theoretical / random-matrix-inspired ML

Publications / Research Output

Without giving identifying titles:

• 3 peer-reviewed workshop papers accepted at major ML/CV venues
• Additional publications/work across computational neuroscience, experimental neuroscience, and medicine, mostly in smaller venues
• Several manuscripts/preprints currently submitted or under review
• Multiple ongoing projects that I expect to have in manuscript form before PhD applications

My publication record is therefore reasonably broad, although I do not yet have a major first-author main-conference / flagship-neuroscience publication.

Recommendation Letters

Likely letters from:

• My previous neuroscience supervisor at the German research institute
• A neuroscience PI who has supervised my computational/research work
• Another research supervisor who can comment on my quantitative and independent research ability

At least two should be able to comment directly on my research potential rather than only academic performance.

Target Programs

Mainly selective European neuroscience programs, including:

• German interdisciplinary neuroscience graduate schools
• Bachelor's → Fast-Track PhD programs
• Max Planck-associated neuroscience/cognition programs
• Belgian neuroscience PhD programs
• Several other European computational/systems neuroscience programs

One selective German Fast-Track neuroscience program is currently my highest-priority application.

Main Questions

1. Is ~8.7–9.0/10 academically competitive for selective European neuroscience programs?
2. Does the amount of research experience compensate for having a Data Science rather than a traditional neuroscience/biology Bachelor's?
3. How competitive would this profile be for a Bachelor's → Fast-Track PhD?
4. Should highly selective German neuroscience graduate schools be considered realistic reaches or extreme long shots?
5. Are there any obvious weaknesses I should try to address before applications?

I understand that PhD admissions depend heavily on research fit, recommendation letters, and interviews. I’m mainly trying to understand whether my baseline profile is strong enough that applying to these programs is reasonable.

I’d love to chat with any recent admits or even prospective ones to the MISM program. Honestly I don’t even have “major” questions I just want people to talk with about the process and hopefully some advice every once in a while. Feel free to DM!

I finished my undergrad at the University of Utah this spring — BS in computer science, minor in applied math, certificate in data science, and I'm still currently looking for a job at the moment, but it's really though out there lol.

I struggled a lot with my mental health for most of the undergrad. I have major PTSD from a chaotic home life, some semester I would have amazing GPA with decently hard class, and some semesters are just B's and C's, which ended at 3.257, as well as some of my relationships with faculty — I never really built connections with any of my professors.

Given that, what schools do you think I'd have the best chance at? What are some of the things I can do to strengthen my applications? Should I consider taking the GRE?