PhD Students - Do you want to learn how to do research?

If yes, check out these free resources.

𝐄𝐥𝐬𝐞𝐯𝐢𝐞𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 𝐀𝐜𝐚𝐝𝐞𝐦𝐲 offers free resources.

Here are 10 of my favorite resources.

𝟏. 𝐇𝐨𝐰 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐚 𝐥𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐫𝐞𝐯𝐢𝐞𝐰 (𝟔𝟎 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- What reviewers look for in a literature review?

- How to conceptualize and write a review?

- Common myths about literature reviews.

Link: https://lnkd.in/gRsWFvdy

𝟐. 𝐇𝐨𝐰 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐠𝐚𝐩𝐬 (𝟏𝟐 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- What is research gap?

- Steps for identifying research gap.

- Tools for identifying research gap.

Link: https://lnkd.in/gvxgD95D

𝟑. 𝐇𝐨𝐰 𝐭𝐨 𝐜𝐨𝐧𝐝𝐮𝐜𝐭 𝐞𝐯𝐢𝐝𝐞𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 (𝟑𝟖 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- What is evidence-based research?

- Stepwise approach for conducting evidence-based research.

- How to enhance reliability of your research?

Link: https://lnkd.in/gMUxXybm

𝟒. 𝐇𝐨𝐰 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬? (𝟏𝟓 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- How to conduct basic search?

- Save searches and set up alerts.

- How to download and export searches?

Link: https://lnkd.in/gKXauFHr

𝟓. 𝐇𝐨𝐰 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐚𝐧 𝐚𝐛𝐬𝐭𝐫𝐚𝐜𝐭 (𝟒𝟎 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- Why a good abstract is important?

- What is ideal length of abstract?

- What to include in the abstract?

Link: https://lnkd.in/g9drbDZF

𝟔. 𝐇𝐨𝐰 𝐭𝐨 𝐬𝐞𝐜𝐮𝐫𝐞 𝐟𝐮𝐧𝐝𝐢𝐧𝐠? (𝟔𝟎 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- How to write grant application?

- What funders look for in grant applications?

- Tips for winning grants.

Link: https://lnkd.in/g-diuMPv

𝟕. 𝐇𝐨𝐰 𝐭𝐨 𝐮𝐬𝐞 𝐆𝐞𝐧 𝐀𝐈 𝐢𝐧 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 (𝟓𝟑 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- How has Gen AI impacted research?

- How to use Scopus AI search tool?

- Future of Gen AI in research

Link: https://lnkd.in/gepXEzBf

𝟖. 𝐀𝐮𝐭𝐡𝐨𝐫 𝐩𝐨𝐥𝐢𝐜𝐢𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐮𝐬𝐞 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐀𝐈 (𝟏𝟏 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- Ethical cases related to Generative AI

- Opportunities offered by Generative AI

- Risks posed by Generative AI

Link: https://lnkd.in/gT4Xg7yP

𝟗. 𝐇𝐨𝐰 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐜𝐨𝐯𝐞𝐫 𝐥𝐞𝐭𝐭𝐞𝐫 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐦𝐚𝐧𝐮𝐬𝐜𝐫𝐢𝐩𝐭 (𝟖 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- Importance of a good cover letter

- How to write strong cover letter?

- What to include in the cover letter?

Link: https://lnkd.in/gFA_pNkD

𝟏𝟎. 𝐇𝐨𝐰 𝐭𝐨 𝐫𝐞𝐬𝐩𝐨𝐧𝐝 𝐭𝐨 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐫𝐬’ 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬? (𝟑𝟕 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- Understanding reviewers’ comments

- How to write response to each comment?

- How to increase your chances of paper acceptance?

Link: https://lnkd.in/gcG7mxrc

PhD Students - Here is an example of a good discussion section.

A good discussion section should answer 6 questions.

1. What is different in your findings compared to previous research?

2. What is similar in your findings compared to previous research?

3. How different sections of your results section correlate?

4. What are the implications of your findings for practitioners?

5. What are the implications of your findings for researchers?

6. What are the limitations or threats to the validity of your findings?

PhD Students - Which tense to use in your research papers?

PhD Students – How to compare 10 papers in 10 seconds?

Meet 𝐒𝐜𝐢𝐒𝐩𝐚𝐜𝐞 – this tool compares papers for you.

Here is how it works.

1. Go to https://lnkd.in/dyirEcYG and log in

2. Click on + 𝑠𝑖𝑔𝑛 and upload the 10 papers.

3. After uploading papers, write your prompt.

𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑡ℎ𝑒 𝑢𝑝𝑙𝑜𝑎𝑑𝑒𝑑 10 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑝𝑎𝑝𝑒𝑟𝑠

4. SciSpace will start comparing the papers.

5. You will see the comparison result on right side.

6. Here you will see various insights with paper numbers.

7. At the end, you will see summary of the comparison.

8. SciSpace compares the papers based on:

✓ Similarities in research themes
✓ Differences in approaches
✓ Relative strengths and weaknesses
✓ Gaps identified across papers
✓ Relationships and building upon each other

9. To trace to each paper, click on the 𝑝𝑎𝑝𝑒𝑟 𝑛𝑢𝑚𝑏𝑒𝑟𝑠

10. To trace to exact location, click on 𝑙𝑜𝑐𝑎𝑡𝑒 𝑃𝐷𝐹.

Where can you use such comparison?

You can use it to:

➝ Understand the related literature.
➝ Position the novelty of your research paper.
➝ Understand niche questions in a research area.
➝ Grasp key insights from a bunch of papers in one go.

Try SciSpace today: https://lnkd.in/dyirEcYG

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PhD Students – How to find references for your paper in seconds?

Meet 𝐋𝐢𝐧𝐞𝐫 – a tool that inserts citations in paper.

𝐇𝐨𝐰 𝐋𝐢𝐧𝐞𝐫 𝐰𝐨𝐫𝐤𝐬?

1. Go to https://lnkd.in/dsgKZV-P
2. Click on 𝐶𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑒𝑟 from the left menu
3. Paste the text in which you want to insert citations
4. Now click on 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝐶𝑖𝑡𝑎𝑡𝑖𝑜𝑛𝑠
5. Liner will insert citations in your text

𝐔𝐬𝐢𝐧𝐠 𝐋𝐢𝐧𝐞𝐫 𝐟𝐨𝐫 𝐜𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐡𝐚𝐬 𝟒 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬

➝ Unlike ChatGPT, it recommends reliable citations
➝ The whole process is very transparent
➝ The citations automatically get inserted in your text
➝ The process is very quick and super easy

Try Liner today for citations: https://lnkd.in/dsgKZV-P

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live.
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other.

Vision Transformers threw that whole approach out.

ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence.
Every patch can attend to every other patch from the very first layer. No stacking required.

That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬:

- Introduction to Vision Transformers and comparison with CNNs
- Adapting transformers to images: patch embeddings and flattening
- Positional encodings in Vision Transformers
- Encoder-only structure for classification
- Benefits and drawbacks of ViT
- Real-world applications of Vision Transformers
- Hands-on: fine-tuning ViT for image classification

The Image below shows

Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face.

The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out.

Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps.

The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images.

𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬
ViT paper dissection
https://youtube.com/watch?v=U_sdodhcBC4

Build ViT from Scratch
https://youtube.com/watch?v=ZRo74xnN2SI

Original Paper
https://arxiv.org/abs/2010.11929

https://t.me/CodeProgrammer

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PhD Students – How to write a systematic literature review draft in 1 day?

A systematic literature review takes 4-6 months.

You can reduce this time.

🎯 Here is how you can write it in 1 hour.

1️⃣ Go to www.gatsbi.com
2️⃣ Select Gatsbi reviewer from the drop-down menu
3️⃣ Enter the topic of your literature review
4️⃣ Gatsbi will generate an outline for review
5️⃣ If you are OK with it, click on write manuscript.
6️⃣ Gatsbi will write the literature review for you.

👉 The literature review contains the following parts

✓ Title
✓ Abstract
✓ Introduction
✓ Methodology
✓ Results
✓ Discussion
✓ Conclusion
✓ References

👉 This polished paper also contains

➝ Diagrams
➝ Tables
➝ Equations
➝ Graphs

Once the paper is ready, you can humanize the text.

Once humanized, you can download it in the following formats.

↳ MS Word
↳ Latex
↳ Markdown

After downloading, you can make any changes you want.

In addition to Gatsbi Reviewer, you can also use:

→ Gatsbi Innovator: Generate ideas before writing
→ Gatsbi Writer: Write research papers

🎗 Try Gatsbi today for free: www.gatsbi.com

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#phd #research #literature #review

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🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning

Both code and weights are available under the MIT license on HuggingFace.

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• Trained from scratch (not a finetune) on proprietary data and infrastructure
• Mixture-of-Experts (MoE) architecture

Models:

🧠 GigaChat-3.1 Ultra
• 702B MoE model for high-performance environments
• Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
• Supports FP8 training and MTP

⚡️ GigaChat-3.1 Lightning
• 10B model (1.8B active parameters)
• Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
• Efficient local inference
• Up to 256k context

Engineering highlights:

• Custom metric to detect and reduce generation loops
• DPO training moved to native FP8
• Improvements in post-training pipeline
• Identified and fixed a critical issue affecting evaluation quality

🌍 Trained on 14 languages (optimized for English and Russian)

Use cases:

• chatbots
• AI assistants
• copilots
• internal ML systems

Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.