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How Indian researchers can bridge the gap between academia and industry (82)

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Starting 45 pts 0 followers
NITTE University · Posted
Neuroscience

My Setup

I run a machine learning lab with 8 PhD students. Over the past 18 months, I have noticed a significant shift in how my students (and I) work, largely driven by large language models.

Things that have genuinely improved:

  • Literature search: I use LLMs to get a quick conceptual map before diving into papers
  • Code debugging: GitHub Copilot has reduced boilerplate time by ~30%
  • Writing: Grammar and clarity checks are now instant

Things I am cautious about:

  • Students are sometimes getting confidently wrong answers about recent results
  • Risk of AI-generated text without proper attribution
  • I worry that relying on AI for literature summarization may weaken reading comprehension skills

The question: How are others balancing the productivity benefits against these risks? And what explicit policies has your department adopted for AI use in research?

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90 Replies

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0
Manish Tiwari Starting 10 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.

0
Ekta Choudhary Starting 45 pts · 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.

0
Replying to Ekta Choudhary
Bhavana Reddy Starting 30 pts · Accepted answer

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

0
Hemant Patwa · Accepted answer

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

0
Bindiya Thomas Growing 65 pts · Accepted answer

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

0
Rekha Shetty Starting 40 pts · Accepted answer

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

0
Sunil Bhattacharya Active 390 pts · 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.

0
Lakshmi Devi Growing 75 pts · Accepted answer

+1 to everything said above. My experience was identical.

0
Aditya Sharma Starting 45 pts · Accepted answer

+1 to everything said above. My experience was identical.

0
Replying to Aditya Sharma
Pooja Nambiar Growing 115 pts · Accepted answer

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

0
Replying to Aditya Sharma
Wajid Khan Growing 110 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.

0
Ishaan Verma Growing 60 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 Ishaan Verma
Shiva Prasad Active 480 pts · Accepted answer

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

0
Manish Tiwari Starting 10 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
Nandita Ghosh Distinguished 720 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
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
Replying to Aditya Sharma
Zara Hussain Growing 55 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.

0
Replying to Aditya Sharma
Tara Singh Starting 5 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 Aditya Sharma
Chirag Mehta Starting 15 pts · Accepted answer

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

0
Uday Bose Starting 5 pts · Accepted answer

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

0
Replying to Uday Bose
Jaya Lakshmanan Growing 50 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 Uday Bose
Kavitha Subramanian Active 365 pts · Accepted answer

+1 to everything said above. My experience was identical.

0
test tester Growing 165 pts · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

0
Sowmya Narayanan Active 310 pts · Accepted answer

+1 to everything said above. My experience was identical.

0
Indira Balan · Accepted answer

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

0
Replying to Indira Balan
Rekha Shetty Starting 40 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?

0
Replying to Indira Balan
Wajid Khan Growing 110 pts · Accepted answer

+1 to everything said above. My experience was identical.

0
Replying to Indira Balan
Royal Dsouza Starting 30 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
Meghana Rao Growing 170 pts · Accepted answer

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

0
Aditya Sharma 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
Revathi Chandrasekaran Active 410 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
Replying to Revathi Chandrasekaran
Chirag Mehta Starting 15 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 Revathi Chandrasekaran
Yogesh Pandey Growing 70 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.

0
Quamar Ahmed Growing 65 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.

0
test tester Growing 165 pts · Accepted answer

FDR (Benjamini-Hochberg) is widely accepted and preferable to Bonferroni when you have many tests and limited power. The key is to be explicit about which correction you used and why.

On replication: No independent cohort is a genuine limitation but not a dealbreaker for rare disease research, where cohort availability is a known constraint. Many high-quality papers acknowledge this and still get published in good journals. Frame it as future work.

Power analysis: Yes, report it. Showing you are aware of the limitation is better than not mentioning it.

0
Replying to test tester
Ekta Choudhary Starting 45 pts · Accepted answer

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

0
Replying to test tester
Kiran Yadav Growing 130 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
Sanjay Kumar Growing 145 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 Sanjay Kumar
Zara Hussain Growing 55 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 Sanjay Kumar
Bhavana Reddy Starting 30 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.

0
Replying to Sanjay Kumar
Dinesh Kulkarni Distinguished 730 pts · Accepted answer

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

0
Dinesh Kulkarni Distinguished 730 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
Replying to Dinesh Kulkarni
Aditya Sharma Starting 45 pts · Accepted answer

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

0
Replying to Dinesh Kulkarni
Yogesh Pandey Growing 70 pts · Accepted answer

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

0
Prakash Iyer Distinguished 640 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?

0
Replying to Prakash Iyer
Fatima Sheikh Growing 95 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
Om Prakash Growing 55 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.

0
Admin NITK · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

0
Wajid Khan Growing 110 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

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?

0
Revathi Chandrasekaran Active 410 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
Bhavana Reddy Starting 30 pts · Accepted answer

+1 to everything said above. My experience was identical.

0
Replying to Bhavana Reddy
Prakash Iyer Distinguished 640 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?

0
Replying to Bhavana Reddy
Arjun Venkatesan Growing 195 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
Xena D'Souza Starting 25 pts · Accepted answer

FDR (Benjamini-Hochberg) is widely accepted and preferable to Bonferroni when you have many tests and limited power. The key is to be explicit about which correction you used and why.

On replication: No independent cohort is a genuine limitation but not a dealbreaker for rare disease research, where cohort availability is a known constraint. Many high-quality papers acknowledge this and still get published in good journals. Frame it as future work.

Power analysis: Yes, report it. Showing you are aware of the limitation is better than not mentioning it.

0
Quamar Ahmed Growing 65 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
Fatima Sheikh Growing 95 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.

0
Indira Balan · Accepted answer

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

0
Replying to Indira Balan
Gopal Naidu · 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
Replying to Indira Balan
Jaya Lakshmanan Growing 50 pts · Accepted answer

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

0
Replying to Indira Balan
Nandita Ghosh Distinguished 720 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
Neha Agarwal Growing 90 pts · 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
Om Prakash Growing 55 pts · Accepted answer

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

0
Replying to Om Prakash
Indira Balan · Accepted answer

Marking this as the accepted answer. Incredibly helpful and specific. Really appreciate you taking the time.

0
Replying to Om Prakash
Ishaan Verma Growing 60 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
Ravi Patel Starting 25 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.

0
Wajid Khan Growing 110 pts · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

0
Replying to Wajid Khan
Manish Tiwari Starting 10 pts · Accepted answer

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

0
Replying to Wajid Khan
Rekha Shetty Starting 40 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
Arjun Venkatesan Growing 195 pts · Accepted answer

Great question.I went through something very similar in my second year.

The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.

0
Wajid Khan Growing 110 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
Karthik Rajan Growing 180 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
Xena D'Souza 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.

0
Royal Dsouza Starting 30 pts · Accepted answer

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

0
Farhan Mirza · Accepted answer

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

0
Bhavana Reddy Starting 30 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
Indira Balan · 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.

0
Priya Nair · 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
Replying to Priya Nair
Uday Bose Starting 5 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 Priya Nair
Deepa Krishnamurthy Distinguished 810 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.

0
Replying to Priya Nair
Chirag Mehta Starting 15 pts · Accepted answer

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

0
Neha Agarwal Growing 90 pts · Accepted answer

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

0
Replying to Neha Agarwal
Preethi Anand Distinguished 690 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 Neha Agarwal
Sanjay Kumar Growing 145 pts · Accepted answer

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

0
Replying to Neha Agarwal
Tara Singh Starting 5 pts · Accepted answer

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

0
Royal Dsouza Starting 30 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.

0
Replying to Royal Dsouza
Chirag Mehta Starting 15 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 Royal Dsouza
Shiva Prasad Active 480 pts · 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
Geeta Rao · Accepted answer

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

0
Bhavana Reddy Starting 30 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.