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Starting 30 pts 0 followers
Indian Institute of Technology Bombay · Posted
Neuroscience Higher Education Policy Linguistics

The honest answer to 'is a PhD worth it in India in 2025'

I will answer this question honestly because I see a lot of either uncritical enthusiasm or doom-and-gloom on this forum.

A PhD is worth it if:

  • You have a specific intellectual problem you are genuinely obsessed with
  • You want a career in research, academia, or high-end R&D at companies that value PhD credentials
  • You are comfortable with a 4–6 year period of relatively low pay and high uncertainty in exchange for intellectual freedom
  • You have a supervisor who is genuinely good at mentoring (this is the single biggest variable)

A PhD is probably not worth it if:

  • You want it for the credential rather than the research itself
  • You are going into fields (software engineering, finance, many business areas) where a PhD does not provide a salary premium
  • You are relying on it to 'figure out' what you want to do.that is what a master's degree is for

The Indian academic job market is brutal. If you want to be a professor at an IIT or IISC, you will likely need postdoctoral experience and a publication record that takes 8–10 years post-bachelor's to build.

But if research is genuinely what you want to do.it is absolutely worth it.

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84 Replies

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Ravi Patel Starting 25 pts · Accepted answer

Respectfully, I think this framing misses the systemic dimension. Individual researchers cannot solve a collective action problem. If you submit to open-access journals and your colleague submits to Nature, your colleague gets the promotion. Until evaluation criteria change at the institutional level.which requires policy intervention.individual choices have minimal impact.

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Replying to Ravi Patel
Tara Singh Starting 5 pts · Accepted answer

CSIR-UGC NET preparation: past papers are the most important resource. Everything else is secondary.

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Replying to Ravi Patel
Hemant Patwa · Accepted answer

Much appreciated. I will restructure my proposal along these lines. The point about societal impact being substantive is something I would have gotten wrong.

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Disha Malhotra Starting 8 pts · Accepted answer

Just to make sure I understand the Zotero workflow correctly.is the .bib file synced to Overleaf automatically every time you add a new paper, or do you need to trigger an export manually?

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Aditya Sharma Starting 45 pts · Accepted answer

As a senior faculty member (25 years in the system), I want to offer a different perspective. Yes, the pressures are real. But I have also seen the other side: students who produce excellent work, publish honestly, and build reputations over time. The system is imperfect but not entirely broken.

What has actually helped my students: choosing venues carefully (a few strong papers rather than many weak ones), developing genuine collaborations rather than transactional coauthorships.

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Replying to Aditya Sharma
Suresh Iyer · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

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Vikram Bhatia Active 420 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Replying to Vikram Bhatia
Manish Tiwari Starting 10 pts · Accepted answer

Just to make sure I understand the Zotero workflow correctly.is the .bib file synced to Overleaf automatically every time you add a new paper, or do you need to trigger an export manually?

0
Replying to Vikram Bhatia
Admin NITK · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

0
Replying to Vikram Bhatia
Wajid Khan Growing 110 pts · Accepted answer

This is very reassuring. I was worried the null result would kill the paper but your framing around transparency and effect sizes makes a lot of sense.

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Preethi Anand Distinguished 690 pts · Accepted answer

Flower (flwr) is the most actively maintained and research-friendly federated learning framework right now. My group has been using it for 18 months on a healthcare project with 4 hospital sites.

Strengths:

  • Clean simulation API that lets you test on a single machine before deploying
  • Good support for custom aggregation strategies (FedProx, FedNova)
  • Active Discord community with quick responses from the maintainers

For non-IID data specifically, look at the FedProx strategy.it handles heterogeneous data distributions much better than FedAvg.

0
Replying to Preethi Anand
Neha Agarwal Growing 90 pts · Accepted answer

Thank you for this detailed answer! A quick follow-up: when you mention cosine learning rate with warm-up, are you warming up the LoRA parameters specifically or the entire model including the frozen backbone?

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Replying to Preethi Anand
Indira Balan · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

0
Replying to Preethi Anand
Fatima Sheikh Growing 95 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Asha Pillai Distinguished 880 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

0
Meena Sharma · Accepted answer

I found that writing a paper abstract first (before the paper) and then reverse-engineering the paper from the abstract helped with clarity enormously.

0
Farhan Mirza · Accepted answer

Just to make sure I understand the Zotero workflow correctly.is the .bib file synced to Overleaf automatically every time you add a new paper, or do you need to trigger an export manually?

0
Rajesh Kumar · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

0
Wajid Khan Growing 110 pts · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

0
Neha Agarwal Growing 90 pts · Accepted answer

I'd push back slightly on the idea that industry collaboration is universally beneficial for academic research. The incentive misalignment is real.

Industry partners want deliverables on a schedule. Academic research is often exploratory and unpredictable. When the two clash, it is usually the researcher who has to compromise.either by rushing results, constraining publication, or steering away from results that are inconvenient for the sponsor.

This doesn't mean industry collaboration is bad. But the terms matter enormously. IP rights, publication rights, and the ability to pursue negative results should be negotiated upfront and protected in writing.

0
Eshan Patil Growing 70 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

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Aditya Sharma Starting 45 pts · Accepted answer

The ethics review situation in Indian universities is actually more complex than most people realize.

There is no uniform national framework equivalent to the US Common Rule. Each institution has its own Institutional Ethics Committee (IEC) or Institutional Review Board (IRB).

For anonymous survey research with no identifiable data and no vulnerable populations: most Indian IECs would classify this as exempt, but 'exempt' still needs a formal determination from the IEC.it is not self-certifying.

My practical advice: go to your institution's research ethics office now, describe what you did, and request an exempt determination in writing. Better to have documentation than to discover the issue during thesis review.

0
Zara Hussain Growing 55 pts · Accepted answer

I have a slightly different take from my experience in industry research. The reproducibility crisis is real but unevenly distributed. Fields with strong engineering culture (computational biology, ML with benchmarks) have actually improved significantly in the last 5 years. The bigger problem is in fields where data sharing is structurally difficult.clinical medicine, behavioral economics.

The ML community's move toward open code and reproducibility checklists has been genuinely effective.

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Replying to Zara Hussain
Xena D'Souza Starting 25 pts · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

0
Farhan Mirza · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

0
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

Flower (flwr) is the most actively maintained and research-friendly federated learning framework right now. My group has been using it for 18 months on a healthcare project with 4 hospital sites.

Strengths:

  • Clean simulation API that lets you test on a single machine before deploying
  • Good support for custom aggregation strategies (FedProx, FedNova)
  • Active Discord community with quick responses from the maintainers

For non-IID data specifically, look at the FedProx strategy.it handles heterogeneous data distributions much better than FedAvg.

0
Wajid Khan Growing 110 pts · Accepted answer

If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.

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Replying to Wajid Khan
Rekha Shetty Starting 40 pts · Accepted answer

Thank you! The reference to MuRIL is particularly useful.I had not considered it as an alternative to XLM-R for Indic languages.

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Replying to Wajid Khan
Admin NITK · Accepted answer

This is exactly what I needed. Thank you so much.I have been going back and forth on this for weeks and this clears it up completely.

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Sunil Bhattacharya Active 390 pts · Accepted answer

Strongly agree with the point about choosing venues carefully. One paper in NeurIPS > five papers in obscure workshops.

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Replying to Sunil Bhattacharya
Kavitha Subramanian Active 365 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Replying to Sunil Bhattacharya
Eshan Patil Growing 70 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

0
Replying to Sunil Bhattacharya
Quamar Ahmed Growing 65 pts · Accepted answer

This is exactly what I needed. Thank you so much.I have been going back and forth on this for weeks and this clears it up completely.

0
Dinesh Kulkarni Distinguished 730 pts · Accepted answer

Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.

0
Meena Sharma · Accepted answer

I have a slightly different take from my experience in industry research. The reproducibility crisis is real but unevenly distributed. Fields with strong engineering culture (computational biology, ML with benchmarks) have actually improved significantly in the last 5 years. The bigger problem is in fields where data sharing is structurally difficult.clinical medicine, behavioral economics.

The ML community's move toward open code and reproducibility checklists has been genuinely effective.

0
Replying to Meena Sharma
Revathi Chandrasekaran Active 410 pts · Accepted answer

Thank you! The reference to MuRIL is particularly useful.I had not considered it as an alternative to XLM-R for Indic languages.

0
Replying to Meena Sharma
Sanjay Kumar Growing 145 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Replying to Meena Sharma
Varsha Pillai Growing 60 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

0
Varsha Pillai Growing 60 pts · Accepted answer

Flower (flwr) is the most actively maintained and research-friendly federated learning framework right now. My group has been using it for 18 months on a healthcare project with 4 hospital sites.

Strengths:

  • Clean simulation API that lets you test on a single machine before deploying
  • Good support for custom aggregation strategies (FedProx, FedNova)
  • Active Discord community with quick responses from the maintainers

For non-IID data specifically, look at the FedProx strategy.it handles heterogeneous data distributions much better than FedAvg.

0
Pooja Nambiar Growing 115 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Ashwin Murthy Distinguished 560 pts · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.

0
Replying to Ashwin Murthy
Lakshmi Devi Growing 75 pts · Accepted answer

The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.

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Replying to Ashwin Murthy
Xena D'Souza Starting 25 pts · Accepted answer

I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.

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Uday Bose Starting 5 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

0
Disha Malhotra Starting 8 pts · Accepted answer

The short answer: it depends heavily on the journal and field, but here is a general framework.

If your null result is well-powered and the hypothesis was reasonable: Absolutely publish it. Null results in well-designed studies are as valuable as positive results. Journals like PLOS ONE explicitly welcome them.

If your study is underpowered: Be transparent about it. Report effect sizes and confidence intervals, not just p-values. A small, underpowered study with honest reporting is still a contribution.

What kills papers is when limitations are obvious but the authors have not addressed them.

0
Replying to Disha Malhotra
Royal Dsouza Starting 30 pts · Accepted answer

Much appreciated. I will restructure my proposal along these lines. The point about societal impact being substantive is something I would have gotten wrong.

0
Replying to Disha Malhotra
Jaya Lakshmanan Growing 50 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

0
Replying to Disha Malhotra
Manish Tiwari Starting 10 pts · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

0
Neha Agarwal Growing 90 pts · Accepted answer

This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?

0
Replying to Neha Agarwal
Amit Joshi Starting 15 pts · Accepted answer

Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.

0
Bhavana Reddy Starting 30 pts · Accepted answer

The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.

0
Replying to Bhavana Reddy
Disha Malhotra Starting 8 pts · Accepted answer

Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.

0
Chetan Jain Growing 100 pts · Accepted answer

Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.

0
Sunil Bhattacharya Active 390 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

0
test tester Growing 165 pts · Accepted answer

The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.

0
Replying to test tester
Chetan Jain Growing 100 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

0
Replying to test tester
Farhan Mirza · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

0
Meena Sharma · Accepted answer

For Zotero vs BibTeX management, I moved to Zotero + Better BibTeX 3 years ago and have not looked back.

My workflow:

  1. Zotero browser connector captures papers instantly from ArXiv, Google Scholar, ACM DL, IEEE Xplore
  2. Better BibTeX generates a clean .bib file that auto-updates whenever I add a paper
  3. Overleaf directly pulls from the .bib file via Dropbox sync

One tip: create a Zotero collection per paper/project. When you export the .bib, export only that collection to keep it clean.

0
Admin NITK · Accepted answer

I have successfully received PMRF (lateral entry, second year). Happy to share what worked in my proposal.

On the research proposal:

  • 60–70% technical approach, 30% context and impact
  • Preliminary results absolutely help. Coursework projects are fine if genuinely relevant
  • The societal impact section should be substantive.PMRF is a national initiative; reviewers want to see why this matters

On citations: Yes, cite your MTech thesis. Framing your PhD as an extension of prior work you did shows continuity of thought.

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Amit Joshi Starting 15 pts · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

0
Replying to Amit Joshi
Bindiya Thomas Growing 65 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

0
Replying to Amit Joshi
Bhavana Reddy Starting 30 pts · Accepted answer

This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?

0
Geeta Rao · Accepted answer

The ethics review situation in Indian universities is actually more complex than most people realize.

There is no uniform national framework equivalent to the US Common Rule. Each institution has its own Institutional Ethics Committee (IEC) or Institutional Review Board (IRB).

For anonymous survey research with no identifiable data and no vulnerable populations: most Indian IECs would classify this as exempt, but 'exempt' still needs a formal determination from the IEC.it is not self-certifying.

My practical advice: go to your institution's research ethics office now, describe what you did, and request an exempt determination in writing. Better to have documentation than to discover the issue during thesis review.

0
Replying to Geeta Rao
Ishaan Verma Growing 60 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Replying to Geeta Rao
Ganesh Menon Growing 140 pts · Accepted answer

Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.

0
Replying to Geeta Rao
Yogesh Pandey Growing 70 pts · Accepted answer

Thank you! The reference to MuRIL is particularly useful.I had not considered it as an alternative to XLM-R for Indic languages.

0
Chirag Mehta Starting 15 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Ravi Patel Starting 25 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

0
Replying to Ravi Patel
Karthik Rajan Growing 180 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

0
Revathi Chandrasekaran Active 410 pts · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.

0
Amit Joshi Starting 15 pts · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

0
Replying to Amit Joshi
Meghana Rao Growing 170 pts · Accepted answer

Really appreciate you taking the time to write this out in detail. This is going straight into my research notes.

0
Replying to Amit Joshi
Preethi Anand Distinguished 690 pts · Accepted answer

The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.

0
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

I'd push back slightly on the idea that industry collaboration is universally beneficial for academic research. The incentive misalignment is real.

Industry partners want deliverables on a schedule. Academic research is often exploratory and unpredictable. When the two clash, it is usually the researcher who has to compromise.either by rushing results, constraining publication, or steering away from results that are inconvenient for the sponsor.

This doesn't mean industry collaboration is bad. But the terms matter enormously. IP rights, publication rights, and the ability to pursue negative results should be negotiated upfront and protected in writing.

0
Replying to Deepa Krishnamurthy
Om Prakash Growing 55 pts · Accepted answer

Much appreciated. I will restructure my proposal along these lines. The point about societal impact being substantive is something I would have gotten wrong.

0
Replying to Deepa Krishnamurthy
Hema Suresh Starting 20 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

0
Ekta Choudhary Starting 45 pts · Accepted answer

This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?

0
Wajid Khan Growing 110 pts · Accepted answer

When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?

0
Replying to Wajid Khan
Om Prakash Growing 55 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

0
Replying to Wajid Khan
Divya Krishnan Growing 85 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

0
Indira Balan · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

0
Chetan Jain Growing 100 pts · Accepted answer

I think you are conflating two separate problems here. The publish-or-perish culture creates pressure, yes.but the solution is not to criticize researchers who are navigating an unfair system. The real problem is with evaluation committees who treat publication count as a proxy for research quality. Fix the evaluation, and the incentives change.

Also worth noting: open review has been implemented in some fields and has its own problems. It can disadvantage early-career researchers who fear retaliation from senior colleagues they have reviewed critically.

0
Divya Krishnan Growing 85 pts · Accepted answer

Flower (flwr) is the most actively maintained and research-friendly federated learning framework right now. My group has been using it for 18 months on a healthcare project with 4 hospital sites.

Strengths:

  • Clean simulation API that lets you test on a single machine before deploying
  • Good support for custom aggregation strategies (FedProx, FedNova)
  • Active Discord community with quick responses from the maintainers

For non-IID data specifically, look at the FedProx strategy.it handles heterogeneous data distributions much better than FedAvg.

0
Lalitha Mohan Active 230 pts · Accepted answer

This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?