LinkedIn Post from Pretha - The PhD Abroad Coach

I just watched 847 PhD applicants make the same Statement of Purpose mistake (Yes, I counted!)

And it's costing them their dream programs.

Last month, I volunteered to review SOPs for grad school applicants through a university mentorship program. What I discovered broke my heart—and then lit a fire under me to share what actually works.

Here's the brutal truth most admissions "experts" won't tell you:

Your childhood inspiration story is killing your chances.

🗣️ The $50,000 mistake I see everywhere

"Ever since I was 7 years old, watching my grandmother struggle with diabetes, I knew I wanted to cure diseases..."

Stop. Just stop.

I've been on 3 PhD admissions committees. We get 400+ applications per cycle. Your personal origin story gets 15 seconds before we move to what actually matters:

Can you do the work?

🔥What happened when I tested this theory

I took two real SOPs from my mentoring group:

SoP A: 2 paragraphs about childhood, family struggles, "passion for science"

SoP B: First para ends with "I reduced protein folding prediction error by 23% using graph neural networks.

Guess who got interviews at UTD?

(Spoiler: The storyteller SoP did not even get in his safety school)

🎯The 3-line formula that changed everything

Line 1: The specific problem you solve

Line 2: Your unique approach/method

Line 3: Why it matters to the field

Forget the fluff. Show the work:

❌ "I worked on machine learning projects"

✅ "I implemented BERT fine-tuning for medical NER, achieving 0.94 F1 on i2b2 dataset; code has 2.3K GitHub stars"

❌ "I'm passionate about climate research"

✅ "My carbon flux model reduced RMSE by 18% vs. existing methods; published in Nature Climate Change"

❌ "Professor X's work aligns with my interests"

✅ "I want to extend Prof. X's 2024 causal inference framework to longitudinal health data, testing robustness across 5 demographic subgroups"

🗣️ The faculty fit mistake that screams "amateur"

I see this in 90% of SOPs:

"I want to work with Professors A, B, C, D, E, F, and G because they're all doing amazing work in my field."

Red flag alert.

This tells me you have no focus. No clear research identity.

Instead, pick 2-3 faculty MAX. For each one:

- One sentence on their recent work (2023-2025 papers)

- One sentence on how you'd extend it

- One sentence on why their lab/resources make it possible

That's it. Specificity beats flattery every time.

🌟 Most applicants end with vague future goals.

Winners include a specific roadmap.

🚑 My 60-minute SOP emergency makeover

If your deadline is looming, do this TODAY:

1. Delete your intro story. Start with research problem.

2. Add specific metrics to every claim you make.

3. Cut faculty list to 2-3 with specific fit explanations.

4. Add Year-1 roadmap with concrete milestones.

5. Proofread names, dates, and technical terms.

Helped? Share with someone who may need it. 

Most people miss the real definition of a PhD...

It’s not just about writing a thesis: it’s about mastering everything.

A PhD means being a:

✍️ Writer

💭 Critical thinker

📊 Analyst

🧩 Problem solver

📅 Project manager

🔍 Researcher

🤹‍♀️ Deadline juggler

💪 Survivor

🗣️ Communicator

Each day, you wear a different hat, juggle countless roles, and somehow keep pushing forward

learning,

adapting,

and creating along the way.

🎓 The truth? Being a PhD is less about knowing it all and more about figuring it all out.

💬 Did I forget any essential role? What would you add to the list?👇 

I don't know if being thirsty about learning something like AI and its branches and getting stuck in a loop of learning new things is good or bad, but I love this weak point. 

For example, at first, I got interested in learning Python and I started watching videos and taking courses about Python for beginners, and it completely could satisfy my demands for getting the attention of a Ph.D. supervisor, but I just started learning machine learning, neural network, deep learning, etc. 

So I deep-dived into an ocean of knowledge that was infinite, and right now I am happy about that, and I think it can be not only satisfying but also practical for my perspectives.

by Aref Jozi, 14 DEC 2022

The power of optimism cannot be underestimated, as it has the ability to shape the future. Individuals who possess a positive perspective towards life and hold the belief that they have the capability to make the world a better place are more inclined to take the necessary steps towards achieving that vision. 

It is crucial to note that the future is not predetermined, and we have the power to determine its outcome through our actions and choices. Optimists who envision a brighter future and work relentlessly towards it can inspire others to join in their efforts towards creating a better world. 

Optimistic individuals are committed to making a difference, whether it is through addressing environmental concerns, promoting technological advancements, or striving for social justice. 

In summary, the future is shaped by those who embrace optimism and are driven to make their visions a reality through dedicated efforts.

Committing yourself to a well-planned schedule is a powerful tool that can help you achieve success and attain your objectives, especially for work, school, or personal projects. 

Such a commitment signifies a promise to utilize your time sensibly, prioritize essential tasks, avoid distractions, and adjust to unforeseen events when necessary. Furthermore, maintaining a strong commitment to your schedule can instill good habits and a sense of routine, which can boost your efficiency and productivity. 

In addition, it can also alleviate stress and enhance your overall well-being by creating a feeling of control and accomplishment as you adhere to your plan. Even though this requires devotion and self-discipline, the benefits far outweigh the effort.

The field of civil engineering is intricate and challenging, as it involves the preparation, creation, implementation, and upkeep of various infrastructure schemes such as airports, buildings, bridges, and roads. 

A group of specialists, including engineers, architects, construction workers, and project managers, need to collaborate and utilize their expertise to complete these projects. Hence, teamwork is crucial in civil engineering to guarantee that these projects prosper. 

Proficient teamwork enables individuals with different abilities and experiences to work together, exchange ideas, and tackle intricate issues. Consequently, this cooperation leads to the accomplishment of a project that complies with the client's specifications, is completed on schedule, and adheres to the budgetary restrictions. 

Furthermore, teamwork guarantees that safety procedures are followed, and potential risks are quickly identified and resolved. In conclusion, teamwork is an indispensable component of civil engineering that contributes significantly to the prosperity and security of infrastructure projects.

As technology continues to advance, machine learning is becoming an increasingly important tool in civil engineering. Machine learning algorithms can analyze large amounts of data, identify patterns, and make predictions that can help engineers design more efficient and cost-effective structures.

In the future, machine learning will play a critical role in optimizing the design and construction of buildings, bridges, and other infrastructure. By analyzing data from sensors and other sources, machine learning algorithms can identify potential problems before they occur, helping to prevent accidents and reduce maintenance costs.

Machine learning will also enable engineers to design structures that are more resilient to natural disasters. By analyzing data from past disasters, machine learning algorithms can identify patterns and predict how structures will behave in future events.

In addition, machine learning will be used to improve the accuracy of simulations and modeling. By incorporating machine learning algorithms into simulations, engineers can create more accurate and realistic models that can be used to test designs before construction.

Overall, the future of machine learning in civil engineering is bright. As technology continues to evolve, it will help engineers design safer, more efficient, and more resilient structures that can withstand the challenges of the future.

(This content and the image are created by artificial intelligence)