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Growing 165 pts 1 follower
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Machine Learning & AI Environmental Science

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7 Replies

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Uday Bose 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 Uday Bose
Tara Singh Starting 5 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 Uday Bose
Ravi Patel Starting 25 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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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.

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Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

[Moderator note] Reminder to keep discussion collegial. We can disagree on ideas.and productive disagreement is valuable.but please avoid personal characterizations. Thank you.

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Lakshmi Devi Growing 75 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.

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test tester Growing 165 pts · Accepted answer

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