Help PhDs to kick-start careers in industry | Co-Founder and CEO @Alma.me | Building a bridge between academia and industry | Speaker | Scientist

Welcome to my profile!

I'm Elena, passionate about helping PhDs kickstart fulfilling careers outside of academia, and connect them with industry relevant positions. 

During my PhD journey, I realized that many PhDs face challenges transitioning from academia to industry. This difficulty often arises due to a lack of knowledge about potential positions, how to craft a resume for the industry, and prepare effectively for a job in that sector.

I've experienced this struggle firsthand. While pursuing my PhD, I felt lost. I knew that I no longer wanted to work in the lab; instead, I wanted to work with people, as that's where my passion lies. I went on my own discovery journey on "what is next".  I focused on the things I loved doing and thought of possible career options- after I identified what is next- I started working toward it by building a network in that area. 

Who am I, and how did I get here?

I recently graduated with a PhD in Medical Sciences from the Karolinska Institutet in beautiful Stockholm. 
As a Co-Founder and CEO of Alma.me my vision is to bridge the gap between academia and industry by providing information to help PhDs understand the array of opportunities after completing their studies and recognise the value of their skills beyond academia. 

If you are a PhD-student, Postdoc or tenure and you want to make the transition into industry follow me for more career advice.


Elena Hoffer, PhD

"If you care too much about money, you're not truly committed." 

This sentence from Dritjon Gruda in the latest Career Nature Magazine captures a mindset that’s deeply embedded in academia - and a big reason why scientists rarely talk openly about money. 

Many academics believe that asking for fair pay makes them look less committed to science. As if caring about your livelihood somehow cancels out your passion. 

Over the years, I’ve met senior academics who never negotiated their salary in their life. 

I’ve also met researchers who couldn’t afford to live near their lab because it would eat up half their salary. 

And often, this is accepted. 

Because science is framed not as a job - but as a calling. Something you do out of passion, not for stability or comfort. Talking about wanting to make enough money to live a comfortable life should be normalised in academia!

During my PhD, when I said I wanted to move into industry, the first response I got was: 
“Oh… so you’re going for the money?” 

That question made me feel terrible. 
But why? 

What is wrong with wanting a comfortable life and meaningful work? 

The article helped spark this reflection - and I hope that for the next generation of scientists, this mindset starts to shift. 

If we want scientists to worry less and focus on what they are good at - doing science - we need to make them feel financially secure.

I’ll link the article in the comments 👇

♻️  Repost to help a friend

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Help PhDs to kick-start careers outside of academia | CEO @Alma.me | Complementing universities services for PhDs & Postdocs to secure jobs outside academia | Speaker I Podcast Host @PostPhDirections

I didn’t leave academia because I don’t love science.
I love science deeply.

I would say most people don’t leave because they stop being curious or scientifically driven. They leave because the way the system is designed doesn’t serve them anymore.

Being a “successful” scientist often comes with enormous sacrifices: long working hours, weekends in the lab, nights that blur into mornings. Life is frequently put on hold. And after all that, you still find yourself wondering whether you can afford a nice dinner with friends.

The hardest part? 
After years of sacrifice, there is no guarantee of a career in academia. It’s a high-risk investment. Only a small fraction will make it - often not just the best scientists, but those with the right timing, networks, or luck. 
Everyone else eventually has to leave.

Leaving academia is not a failure. But for many, it feels like finally allowing themselves to pursue a life they’ve been postponing for years.

- Kids can wait until after my postdoc.
- Getting married doesn’t fit right now.
- Buying a home feels impossible while moving countries every few years.

While others move forward with their lives, an academic career often asks you to keep waiting. And even when you “succeed,” it can come at a very high personal cost.

I left because the system no longer aligned with my values. 
And I think this is true for many academics.

Wanting to have more stability is a valid reason to leave - even if you love what you do 💙

Your PhD ≠ one career path.

One of the biggest misconceptions I had during my PhD was that my options were academia — or R&D in industry.

After all, I had spent years at the bench.

What I didn’t realise at the time was that my PhD had given me far more than lab techniques - and that many industry roles value those skills far beyond bench work.

Here’s a simple way to think about industry roles for PhDs ⬇️

If you enjoy coding & working with numbers
 → Data Scientist · Data Analyst · Decision Scientist

If you enjoy discovering or building new knowledge
 → Research Scientist · Applied Scientist · R&D

If you enjoy explaining complex ideas clearly
 → Medical Writer · Medical Science Liaison (MSL) · Technical Writer

If you enjoy big-picture thinking & decision-making
 → Product Manager · Strategy Analyst · Consultant

If you enjoy working with customers & real-world problems
 → Technical Sales · Field Application Specialist

What all of these roles have in common?
They value PhD skills: research, analysis, problem-solving, writing, learning fast.

♻️  Share to help a friend 

And if you want to listen to some real transitions stories of PhDs check out my Podcast: PostPhDirections - link in the comments 👇

Many PhDs (and the world) underestimate the skills they’ve developed along the way. 

If you pause and look back to when you started your PhD, think about everything you can do now that you couldn’t do then - from establishing collaborations and managing projects, to writing, problem-solving, ordering reagents, and keeping complex work moving forward. 

A PhD doesn’t just give you technical expertise. 
It builds powerful transferable skills: critical thinking, communication, resilience, and persistence. 

We’ve worked with PhDs who moved from psychology into fintech, from computational neuroscience into medical writing, and from toxicology into project management - all by focusing on these skills rather than their subject area. 

You are more than your PhD topic. 

And your skills can take you further than you think!

♻️  Repost to raise awareness and help another PhD 

Happy Tuesday 🙌

Your PhD is work experience

Is it? 

This is one of the most common questions I get from PhDs who want to transition into industry. 

And I get why it’s confusing. 

We’re trained to think of a PhD as “education.” 

So it feels strange to suddenly call it work experience. 

But here’s the thing: 
Industry doesn’t define work experience as “having a job outside academia.” 

Work experience means: 
• Applying skills 
• Solving real problems 
• Dealing with uncertainty 
• Communicating results 
• Collaborating with others 

For example:
Designing a study, managing timelines, dealing with failed experiments, aligning with collaborators, and presenting outcomes to different audiences - that’s professional experience.

That’s exactly what happens during a PhD. 
You agree?

You don’t just learn about things. 
You do things. 

So yes - your PhD is work experience. 
And it belongs under professional experience in your resume.
 
If you’ve been unsure about this, you’re not doing it wrong. 
You just needed a better definition. 

You’ve got this. 

And if you feel stuck in your transition we can help. Check if our Job Accelerator Program aligns with your needs. Link in the comments 👇 

♻️  Repost to help demystifying what a PhD actually is and does!

There is no single path after a PhD.

Becoming a professor is just one of many possible options. In fact, around 90–95% of PhD graduates move on to roles outside the traditional professorship track — including positions within academia such as administration, staff scientist roles, or facility management.

Outside academia, PhDs transition into industry, government, or NGOs, where they contribute as data scientists, project managers, analysts, scientists, and much more.

The idea that a PhD automatically leads to a career as a professor is outdated. Yet many PhDs are still unaware of the wide range of alternatives, as these paths are rarely discussed within universities.

There is no one “right” path after a PhD — whatever you decide to do is valid

♻️ Repost to help a friend

Happy Saturday 💪

Most interviews ask the same questions:

• Tell me about yourself
• Walk me through your CV
• Why this role / company?
• Salary expectations
• Behavioral + technical questions

Some look simple. But in most cases they aren’t. Many PhDs get lost in academic jargon and detail for 10+ minutes, and this is a recipe for losing your listener.

Interviews rarely test the question itself.
They test what sits underneath it.

Once you understand this, interviews get easier. You’ll realize they’re not interested in every detail of your academic history.

So let's talk about it!

“Tell me about yourself”
→ Can you structure and prioritize information?

“Walk me through your background”
→ Can you translate experience into relevance?

“Why this role / company?”
→ Are you intentional - and do you understand their problems?

“Salary expectations”
→ Do you know your market value?

Behavioral questions test:
→ self-awareness, judgment, communication

Technical questions test:
→ how you think, apply knowledge, handle uncertainty

Here’s what many PhDs miss:
👉 Interviews are not exams.

You’re there to show how you think, communicate, and learn.

If interviews feel stiff or unnatural, it’s often not a skills problem. 

Most often, PhDs feel they have to put on a mask and perform. Once you allow yourself to be yourself, things get easier and more authentic.

And yes - this can be learned. We teach it everyday in our job accelerator program.

You got this 🙌 

♻️ Share to help a friend

Happy Tuesday 🙌

When I was doing my PhD, I thought networking was really awkward.
I didn’t like it, and honestly, I didn’t really know how to do it.

Looking back, most of that resistance came from the fact that I completely misunderstood what networking actually is.

Once I reframed it, everything changed.

I stopped thinking about networking as asking people for favors or jobs and started seeing it for what it really is: relationship building.

That shift alone helped me build both a global online network and a local, in-person one.

So how did I actually get started on LinkedIn?

Very simply. But still very uncomfortable
 
I looked for people who shared similar interests to mine - in my case, other founders and PhDs.

I commented on their posts. I engaged with their ideas.
And by showing up consistently, relationships started to form.

Some of those people I later met for an online coffee chat.
Some I’ve collaborated and partnered with.
Some I’ll probably meet in the future.

And with others, we might simply stay “LinkedIn friends” — and that’s perfectly fine too.

The key is this: find a point of connection and engage around it regularly.
The basis of any real relationship is trust - and trust is built by showing up, again and again.

My biggest tip?
Go into every conversation with curiosity, not expectation.

Does that mean you shouldn’t have a strategy? 
Absolutely not.

But what I’ve learned is that when you follow your genuine interests, you naturally connect with people who care about the same things.

For example, I’ve built a strong network with many PhDs here on LinkedIn.

Some create content on similar topics, others on completely different ones - but what connects us is that we all want to support PhDs: some during their PhD journey, others in their careers beyond academia.

That shared mission makes conversations easy and meaningful.

If you’re not sure where to start, ask yourself:
What topics am I genuinely excited about professionally?

Then start connecting. 
Start commenting.

And if you’re stuck wondering what to comment, here are a few easy options:
- Share your perspective
- Add your own experience
- Contribute an additional insight or resource

Whatever it is - just get started.
That’s how relationships (and networks) are built.

What began as me figuring this out for myself is now what I do every day with PhDs who want to move into industry.

If networking, positioning yourself, or breaking into industry feels unclear -
 you’re not alone.

Curious how we support PhDs at Alma.Me? You can learn more on our website.

Happy Tuesday 🌻

One of the biggest mistakes we see PhDs make in interviews is treating them like an exam.

They focus on saying the right thing.
They memorize answers.
They try to sound impressive.

And in the process, they lose the most important part: the human connection.

An interview is not an exam.
It’s a conversation.

The people interviewing you are human.
And humans hire humans.

What we see again and again in the Alma.Me Job Accelerator Program is this:
PhDs prepare answers by heart.

They sound polished - but also robotic.
There’s no rapport, no flow, no real connection.

And then something interesting happens.
Some candidates come to an interview exhausted.
They’ve had too many interview  so much that they can’t “perform” anymore.

Others walk in thinking:
 “I don’t stand a chance anyway.”

So they stop trying to impress.
They let their guard down.

 They speak naturally.
 They listen.
 They connect.

And that’s often the moment they get hired.
Not because they suddenly became more qualified - but because they finally showed up as themselves and build real connection with the hiring team.

You don’t need to pretend to be an expert.
You already are one.

♻️  Repost to help a friend 

This is exactly what we train PhDs on inside our program: how to stop performing and start connecting in interviews.

👉 If you want to learn this step by step and get coached on it, apply to the Alma.me Job Accelerator Program here: link in the comments

Why reading job descriptions felt impossible as a PhD 

Because as a PhD, I was trained to: 
– Meet every requirement 
– Avoid overclaiming 
– Only apply when fully qualified 

But industry works differently. 
What I learned the hard way: 
Job ads describe an ideal, not a checklist 

- Transferable skills matter more than exact tools 
- You’re evaluated on patterns, not perfection 
- Potential counts - even if no one says it out loud 
- Confidence is read as competence 
- Applying is part of learning, not the final exam 

Through the work I do with Alma.Me I see many PhDs get stuck- just like I did. Thinking you need to match 100% of the description and then get discouraged.

Don't self-reject. I could tell you so many stories of PhDs who thought something was out of reach - they dared and succeeded! 

The perfect candidate does not exist if you meet 70% of the requirements – APPLY  

This shift alone will change the way you approach applications.

♻️  Repost to help a friend 

Happy Wednesday 💙

Academic merits ≠ industry jobs

When PhDs move from academia into industry, they often highlight the same things that opened doors in academia: publications, subject-matter expertise, academic achievements.

That makes sense - it worked in academia.

But in industry, different things are valued.
Hiring managers care less about what you studied and much more about how you work and what were the results and outcomes of your work.

What actually matters:
- How you solve messy, real-world problems
- How you communicate with non-experts
- How you manage projects with unclear outcomes
- How you collaborate, adapt, and keep going when things don’t work

The good news?
You already built these skills during your PhD.

The challenge is this:
 👉 You were trained to talk about knowledge, not outcomes and results. 

If you’re moving into industry, stop asking:
 “Is my research relevant?”

Start asking:
 “Which skills did I use - and how do I show them that I can do the job?”

That shift changes everything.

You got this!!!

♻️  Share to help a friend 

Happy Wednesday 🙌  

---------------
If you want help moving from academic framing to industry-ready positioning, you can check out my website to see how we support PhDs in that process.

I read this article published in The Wall Street Journal and immediately recognized many of the PhDs we work with in it.

Jeremy did everything “right.”
PhD from a strong institution.
In the right city.
In the right industry.

And still: hundreds of applications, no traction, financial pressure, and the growing sense that the path he planned for simply isn’t there anymore.

This is often the moment when PhDs come to us.

Not because they suddenly lost their skills - but because the environment around them changed. 
Funding slows. Hiring freezes spread. Entire ecosystems that once felt stable become unpredictable. And people who were told they were “on a safe path” find themselves questioning everything.

What strikes me about this article is how clearly it shows that this isn’t an individual failure. 
The talent is still there. 
The training is still there. 
What’s missing are the opportunities PhDs were implicitly promised would exist.

Many of the PhDs we work with are in a similar position: they’ve done what they were told would lead to a smooth transition into industry, and yet they’re stuck. 

They read job ads and feel strangely disconnected from them - as if the roles are asking for something they have, but in a language they were never taught to speak.

So they apply more. 
And then more. 
And when that doesn’t work, the self-doubt starts creeping in.

But the problem is rarely a lack of ability. It’s a lack of translation.

Academic training teaches depth, rigor, and independence. 
Industry hiring - especially in a downturn - prioritizes clarity, applicability, and proof of impact. 

When those two worlds don’t align, even very strong PhDs can end up invisible.

Articles like this one are unsettling - but they’re also clarifying. 
They show that relying on a single, linear narrative (“PhD → biotech job → stability”) is risky, not because the PhD is a bad choice, but because markets shift faster than training models do.

For the PhDs who read this and feel anxious or discouraged: that reaction makes sense. You’re responding to real structural changes, not personal shortcomings.

And for many, this is the point where they stop asking “What’s wrong with me?” and start asking “How do I position my skills so they actually make sense outside academia - and across different contexts?”

That shift is often where things begin to move again.

♻️  Share to help a friend 

Want to learn how to align your skills, interests, and values beyond the obvious job choices?

We’re at Alma.Me hosting a free live session where we match PhD skills to industry roles - in real time, with real examples.

Link in the comments 👇

Dear PhDs, rejection is part of the application process....


It’s normal - and yet it stings. It does something to you.
But it says nothing about your personal worth or the value of your skills.

You are not going to be a fit for every position you apply for.
My best recommendation is to expect rejection - because it will come.

But also remember: every no brings you closer to a yes.
And this isn’t theory - it’s what we see over and over again in our job accelerator program where we work with PhDs from various fields.

We had PhDs who went through 17+ interview processes before the yes came.

We had PhDs who made it to the final round - and lost to candidates with more industry experience.

We had PhDs who were promised an offer - and had the position cancelled last minute.

We had PhDs who passed the recruiter screen - but never made it to the hiring manager interview.

And here’s the good news: they all eventually made it.

They turned many no’s into one yes.
Ultimately, you only need one yes - for a position you actually love.

And for most PhDs, the hardest part isn’t competence.
It’s endurance.

If you’re in the middle of this right now and feel like giving up, know this:
your story is not a single one. It’s the story of many PhDs - and it can and will end positive.

👉 If you want structured support to navigate rejections, interviews, and the hiring process as a PhD, apply for our job accelerator program here: 

Happy Monday 🙌

Is doing a PhD worth it?

There are probably as many opinions on this as there are PhDs.

So here’s my take.

Whenever we ask whether a PhD is “worth it”, we first need to ask:
worth it by which criteria?

Are we talking about:
- learning and training?
- financial compensation?
- career optionality?
- status and recognition?

Because depending on what you evaluate, the answer will be very different.

Another important point:
👉 The value of a PhD differs massively by country and industry.

Especially in STEM, PhDs tend to be highly valued in places like the US and Germany - both in perceived status and compensation. 
Over the years, through many conversations, I’ve learned that the same degree can open doors in one country and be almost irrelevant in another.


So let’s break it down.

1. If your goal is an academic career:
Be very clear-eyed about what that means. It’s highly competitive, structurally uncertain, and only a small fraction make it long term.

2. If your goal is growth and learning:
Honestly? I absolutely recommend doing a PhD.
It challenges you in ways few things do. I often describe it as running a small startup: you wear all the hats, constantly problem-solve, deal with failure, and keep going anyway.

If you move countries for your PhD, the growth is even bigger - intellectually and personally.

3. Financial payoff (jobs in industry):
This one depends heavily on field, country, and industry.
We’ve seen PhDs walk into roles paying 150–175k. Others start closer to 100k.
Could you have made more by choosing a different path? Maybe.
But it’s not a simple yes or no.

4. Being a door opener:
Coming from Germany, where education still carries a lot of weight (yes, “Dr.” is literally on your passport), I do think a PhD can still open doors - especially in Pharma and biotech. In some environments, you will hit a glass ceiling without it.

5. Subject-matter expertise:
If you want to deeply immerse yourself in one topic for 4–5 years and truly become an expert - there’s no better way.

6. Mobility:
One of the most underrated perks.
PhDs and postdocs allow you to move internationally, attend global conferences, and work in incredibly international environments.

So is a PhD worth it?
👉 There is no universal answer.

For me personally:
Yes. I would do it again.

I did a PhD in Immunology. I don’t use the subject-matter expertise today - but I do use the skills every single day: grit, creative problem-solving, dealing with failure, trying again.

I still think like a scientist. I approach problems like a scientist.

Is shifting that mindset into industry always easy?

Probably better to ask Angela Priest, my co-founder at Alma.Me - she has 20+ years in business and has been working with me for the past three years 😉

Curious how others see this - was your PhD worth it, and by which criteria?

♻️ Reshare to raise awareness and help a friend

🎙️ New PostPhDirections episode is out!

In this episode, I’m joined by Benjamin Weber Weber,  the Vice President of Translational Research at Novo Nordisk, to unpack what it really takes for PhDs to stand out in today’s competitive pharma job market.

Ben hired PhDs in Pharma in the US, and across different countries in Europe.

This episode is packed with a lot of practical advice on how you can prepare for the transition into Pharma R&D

We talk about:
- How Pharma  hiring has changed - and what skills matter most now
- Why understanding the Pharma R&D value chain is a key differentiator
- Common CV/resume and application mistakes PhDs make (and how to fix them)
- What hiring managers actually look for beyond publications and technical skills

If you’re a PhD or postdoc thinking about (or struggling with) the transition from academia to industry, this episode is packed with practical, honest insights from someone who hires globally.

🎧 Listen to the full episode on Spotify or my new YouTube channel - links in the comments  ⬇️

Happy Friday 🙌

“What should I do after my PhD?” feels often impossible to answer.
There are so many roles out there.

For many PhDs, it’s genuinely overwhelming.

Should I keep doing bench work?
If not — what else can I do?

Over the years, we’ve helped PhDs move into:
- Data Science roles at fintech companies
- Quantitative Modelers at insurance companies
- Project Management roles in healthcare
- Medical writers in Pharma 
…and many more.

The truth is:
You can do a lot after a PhD.
What you can do depends on:
- Your skills
- Your interests
- Your values
- And the kind of life and work situation you want to create

But for most PhDs, the hardest part isn’t having options.
It’s mapping their skills to real industry roles - and understanding what those roles actually look like inside companies.

That’s why we at Alma.Me decided to host a live mapping session where we show you exactly how to approach this in practice.

The session is led by my co-founder Angela Priest. 
She brings over 25 years of industry experience, having hired across all levels and a wide range of roles. Most importantly, she’s seen first-hand how many different roles truly exist inside companies - far beyond what job ads suggest.

📅 Join us for a FREE session on January 15, 6PM CET I 10 AM MST

We’ll map a PhD’s skills and interests to concrete roles in industry.
👉 Sign up here: https://lnkd.in/dVuaRrr4

I finally started something I’ve been thinking about for a long time.

I didn’t start earlier because the perfectionist in me kept holding me back - something I’m actively trying to unlearn from academia (still two years later 😅).

But I finally created my first YouTube video.
It’s not perfect, and that’s okay.

One thing business has taught me: do first, improve later.

So here it is - my first video of many:  Is my PhD work experience?

Each week, I’ll be answering common (and often stressful) questions PhDs and postdocs have about transitioning into industry.

This week’s question:
Does the first job after academia need to be the perfect one?

👉  If this resonates, please hit the subscribe button to the channel. 
I already have a few other videos on the transition from academia to industry - and while it’s not perfect yet, it is authentic.

See you on YouTube: https://lnkd.in/dJbpkcXa

This PhD tried to move to industry since March 2025.
She did what so many PhDs do: applied without any strategy

- 300 applications
- A few interviews
- One final-round interview where she was almost certain she’d get the job

And then the rejection came.

That was the moment she realized something had to change.

Not her skills.
Not her PhD.
But how she was approaching the transition.

She found our Alma.Me Job Accelerator Program through ChatGPT (still one of my favorite stories).

She joined us in September.

In October, she started applying with the resume we built together - one that finally translated her experience into industry language.

What happened next?
Some weeks she had up to 4 interviews.
In November, she accepted a job offer as a Project Manager.

This wasn’t luck.
It was the result of strategy, clarity, and consistent follow-through - exactly what we focus on inside the program.

If you’re applying, interviewing, and still getting stuck at “almost” - you don’t need more applications.

You need clear positioning, honest feedback, and a system that works.

👉 If you’re struggling and want structured support at every step, apply to the Alma.Me Job Accelerator Program. 
Apply here: https://www.alma.me/

Happy Wednesday ✨

Many PhDs primarily consider bench roles when moving into industry.

However, there are alternative career paths for PhDs -  one example is scientific business development.

Yasmine Penn is currently hiring for roles across the US. If this sounds like you, reach out to her 👇

🧬 From the bench to the boardroom - ever considered this path?

For many PhD scientists, moving away from the lab doesn’t mean moving away from science.

Scientific Business Development offers a powerful route into industry for researchers who enjoy big-picture thinking, collaboration, and translating complex science into real-world impact.

We’re currently supporting Scientific Business Development opportunities across the USA, ideal for PhD-level scientists looking to make their first commercial move.

Who this suits:

🔬PhD scientists (Immunology, Pharmacology, Cancer Biology, Genetics or related fields)
🔬Strong hands-on in vivo experience (mouse models)
🔬Curious, commercially minded researchers - no prior BD experience required

Over time, this path builds commercial awareness, client-facing confidence, and cross-functional influence - with a front-row seat to how science becomes products.

📍 Now hiring across multiple US locations
📩 Interested in learning more or applying?
Contact Yasmine Penn
📧 yasmine@greenlsr.com

www.greenlsr.com

#ScientificBusinessDevelopment #PhDJobs #LifeScienceCareers #FromBenchToBusiness #BiotechJobs #CRO #IndustryCareers #GreenLifeScience

It took 6 days. 
 
That's how long it took one of the PhDs I'm working with to land his first interview after he started applying. 
 
What changed? 
-We taught him how to identify and reach out directly to hiring managers 
- We overhauled his resume to focus on impact 
 
He went from: 
 
"Optimized targeted therapies through comprehensive in-vitro testing and data analysis, driving drug viability insights through molecular biology, pharmacokinetics, and chemical analysis." 
 
To: 
 
"Conducted a rapid screening of 50 peptide compounds over a 6-week period for glioblastoma drug discovery, partnering with chemistry, prioritizing and identifying 6 viable compounds for preclinical in vivo testing." 
 
The same person, the same work, but one version shows the impact, the other describes the process. 
 
Hiring teams don't have time to translate your resume. 
 
Do it for them. Make it easy for them to say yes!
 
___________________________ 
 
Are you a PhD struggling to get a job in industry? Check out our job search program here: https://www.alma.me/

Happy Friday 🙌

🩺I take researchers from goals to published results 🇮🇳 🇬🇧 Founder II Academic Writing Mentor || AI researcher || Homeopath ✅️Need Research Solutions❓️ Go to my ABOUT section.🔻

I specialize in testing AI tools for literature reviews, academic writing, editing and statistical analysis, sharing insights that enhance research productivity and workflow.

As a PhD in Life Sciences, I am passionate about guiding academics and PhDs from research concept to completion, ensuring they have the right tools, methodologies and mentorship to succeed.

🎯 How I Help Researchers & PhDs??

Through 

🟪 RESEARCH MADE CLEAR 🟪

I offer consultancy services in:

✅ Scientific Writing & Editing: Ensuring clarity, coherence and impact in research papers.
✅ PhD Mentorship & Research Methodology: Supporting scholars in thesis writing, research design, and proposal development.
✅ Data & Statistical Analysis: Providing expertise in biostatistics, data analysis and result interpretation.
✅ Faculty & Student Development: Training in AI-powered research tools & ethical considerations.
✅️ Webinars & Workshops: How to use AI tools for Literature Review, research writing and Research Management, ethically.

🔍 My Experience & Research Contributions:

With 10+ years of experience across multidisciplinary research projects, I have:
📌 Led & coordinated multiple research trials and longitudinal surveys as a Research Manager.
📌 Published 8+ peer-reviewed papers in evidence-based medical writing.
📌 Developed the MAPNI questionnaire—a validated tool assessing perceived stigma towards mental illness in India.
📌 Taught Research Methodology & Biostatistics as an 'Assistant Professor', mentoring 500+ academic professionals in thesis & dissertation writing.
📌 Supervised successful scholarship applications for the Short-Term Studentship in Homoeopathy (STSH, CCRH, India).

📢 Let’s Connect
✅️Need expert guidance in research writing, AI tools or statistical analysis?
✅️Looking for collaborations, training sessions or mentorship?

💌 Email: dps@researchmadeclear.com
📩 DM me here on LinkedIn!

Dr Priya Singh PhD💜MD (Hom.)

More research is needed’ is not a research gap

Saying “there isn’t much research” is not enough. 
Reviewers want to know exactly what is missing and there are different kinds of gaps.

Here are some of the most important ones researchers should recognize:
 🔹 Evidence gap – We simply don’t have enough solid data yet.
 🔹 Knowledge gap – We don’t fully understand why or how something works.
 🔹 Practical gap – Research exists, but it’s not being used in real settings.
 🔹 Population gap – Certain groups are left out of studies.
 🔹 Methodological gap – The tools or methods used so far are weak or outdated.
 🔹 Theoretical gap – No strong theory explains the issue clearly.
 🔹 Geographical gap – Research is focused on some regions and ignores others.
 🔹 Empirical gap – Important variables haven’t been studied together.
 
When you name the type of gap, your study stops sounding general and starts sounding necessary.

If you’re writing a proposal or thesis, start by asking:
 What kind of gap am I actually filling?

PS: What would you add here?
Share in the comments

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Dr Priya Singh PhD💜MD(Hom.)

Helping PhDs & researchers complete and publish high-quality research PhD mentor || Thesis reviewer || Academic writing expert Training research professionals in working with AI

Before your next submission, read this 👇 

If you’re trying to publish in a good journal and feeling stuck, this paper is genuinely worth your time

It walks you step by step through what actually makes a paper publishable — in plain, practical terms.

Instead of vague advice, it explains:
✅ What editors and reviewers look for in a strong paper
✅ Why many solid studies still get rejected
✅ How to write each section — title, abstract, introduction, methods, findings, discussion, and conclusions — in a way that strengthens your chances of acceptance
✅ Common mistakes that quietly kill good manuscripts

What makes it especially helpful is that it focuses on presentation, structure, and clarity.

If you’re:
🔹 revising a manuscript
🔹 preparing your first journal article
🔹 mentoring students on academic writing
…this paper works like a behind-the-scenes guide to the review process.

Definitely one to read with your own draft open beside you. You’ll start spotting exactly where your paper can be sharper and more convincing.

PS: If you could get expert help on just one section of your paper, which would it be?

Share in the comments

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HOW TO WRITE REALLY GOOD ARTICLES 

Publishing in Q1 or high-impact journals is not about luck 😯 

This editorial makes one thing very clear 

most papers are rejected not because the research is weak, but because it sends the wrong signals.

✅ Lack of originality.
✅ Unclear contribution.
✅ Narrow topics.
✅ Flawed design.
✅ Poor writing flow.
✅ Out-of-scope submissions.

These are not small issues. They are instant filters.

What I appreciated most is how practical the message is:
 
1. Editors look for generalisable insight, not local descriptions  
2. Theory and contribution matter, even in applied work
3. Methodological rigour is expected, not optional
4. Writing quality is part of scholarly quality

If you are submitting repeatedly and getting desk-rejected, this editorial is worth reading slowly and strategically.

PS: What was the hardest publishing lesson you had to learn the slow way?
Share in the comments

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-----------------------------------------------------------------------------------------------

I’m Dr Priya Singh Founder, Research Made Clear 
I offer review, academic editing, and proofreading to help improve quality of your resaerch papers and thesis before submission. DM if you’d like support.

Publishing in Premier & Q1 Journals

If your qualitative paper ends at themes, you may be stopping too early.

Rigor in qualitative research is not just about coding neatly or reporting themes clearly. 

It is about thinking conceptually and being intentional about how findings are elevated into a model that others can use, test, and build on.

Process transparency matters most.

When readers can see how you moved from raw data to ideas, your work becomes easier to trust, teach, and replicate across disciplines and methods.

If you struggle with the question
 “I have good themes… now what?”
 this paper is worth your time 👇 

PS: Is your qualitative paper descriptive or conceptual?
Share in the comments

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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Hi I am Dr Priya, Founder Research Made Clear

I help researchers turn qualitative analysis into publishable papers. Need help? 

Feel free to DM.

Step by Step Process of Thematic Analysis in Qual Research

No one teaches you the real rules of publishing ❌ 

PhDs are trained to collect data, run analyses and read theory

But the rules that actually decide accept or reject?

Those are rarely taught.

Academic publishing operates on a set of tacit rules about contribution, writing, positioning, and reviewer expectations that many supervisors assume you already know.

PhDs struggle because they were never shown how the game is really played.

PS: Which of these hidden rules did no one tell you early in your research journey?

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100 RESEARCH RULES OF THE GAME

A strong conclusion doesn’t close the paper. It opens the conversation.

Your conclusion is not a summary.
It’s your final chance to shape how the reader remembers your work.

I suggest my mentees to follow these for writing a strong conclusion 👇

1. Start by answering the research question clearly.

Say it plainly.
What did you find overall?
If someone only reads your conclusion, they should still understand the main takeaway.

2. Zoom out before you close

Move from specifics to the bigger picture.
Explain what your findings mean for the field, practice, policy, or future research.
This shows maturity, not exaggeration.

3. Link back to the problem you started with

A good conclusion feels like a full circle.
Remind the reader why the problem mattered in the first place and how your study helped address it.

4. Acknowledge limitations calmly

One or two sentences are enough.
State them factually, without apologising or weakening your work.
Confidence comes from transparency.

5. End with forward direction, not repetition

Don’t restate results again.
Instead, point forward:
 – What should happen next?
 – What questions remain open?
 – What could others build on?
That’s how research stays alive beyond the paper.

Save this if you’re writing a paper, thesis, or dissertation.

PS: What do you personally struggle with most when writing conclusions?
Share in the comments.

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Getting a paper published isn’t about “luck”
It’s about fit, clarity, and discipline.

This checklist from SAGE Publishing quietly reminds us of what many early researchers miss:

✅A good paper answers a real question, not just a gap for the sake of a gap
✅Journals care deeply about scope, structure, and audience
✅Small things (word limits, references, blind review rules) matter more than we think
✅Editors want clarity, not cleverness
✅And publication is as much about preparation as it is about results

Strong research fails every day because it wasn’t positioned well.

Before submitting, ask yourself: Does this journal want the paper I’ve written or am I hoping they will?

PS: What’s one lesson about publishing you learned the hard way?
Share in the comments

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Goldmines for learning how real PhDs are written 👇 

High-quality, free PhD thesis repositories worth bookmarking:
1. 1. Global & Multi-Institution Aggregators
✅ Open Access Theses and Dissertations (OATD)
🔗 https://oatd.org
✅ The Networked Digital Library of Theses and Dissertations (NDLTD)
🔗 http://www.ndltd.org
✅ EBSCO Open Dissertations
🔗 https://lnkd.in/eCiUEiGb
✅ EThOS (British Library)
UK doctoral theses with many full-text downloads.
🔗 https://ethos.bl.uk
✅ Thesis Commons
A community-driven open repository for author-deposited theses and dissertations.
🔗 https://thesiscommons.org/

2. National / Regional or Subject-Specific Repositories
✅ Shodhganga (India)
Large open repository of Indian PhD theses.
🔗 https://lnkd.in/eRqD6Qsk
✅ Swedish University Dissertations (dissertations.se)
Index of dissertations in English from Swedish universities, many with PDFs.
🔗 https://lnkd.in/eHTSr9Cn
✅ theses.fr (France)
Access point to French doctoral theses across French institutions.
🔗 https://www.theses.fr/

3. Institutional Open Repositories 
✅ MIT DSpace
Theses and dissertations from MIT.
🔗 https://dspace.mit.edu
✅ Harvard DASH
Harvard University open access repository.
🔗 https://dash.harvard.edu
✅ Cambridge Apollo
University of Cambridge open repository.
🔗 https://lnkd.in/e86rnQn2
✅ SUNScholar (Stellenbosch University)
South African university theses.
🔗 https://scholar.sun.ac.za
✅ OhioLINK ETD Center
Consortium repository for theses from participating Ohio universities.
🔗 https://etd.ohiolink.edu/
✅ eScholarship (University of California)
Open access repository including UC theses/dissertations.
🔗 https://escholarship.org/

4. Tools & Directories that Aid Thesis Discovery
✅ BASE (Bielefeld Academic Search Engine)
Indexes open access content from repositories worldwide (theses included).
🔗 https://lnkd.in/eWbpj-hv
✅ WorldCat (free advanced search)
Global library catalogue where you can filter for theses/dissertations.
🔗 https://www.worldcat.org
✅ OpenDOAR / ROAR
Directories of open access institutional repositories (many contain theses).
🔗 https://lnkd.in/eDvBvcAm

Here is how PhD scholars can benefit from open thesis repositories:
• See how strong research questions are framed
 • Understand chapter structures that actually passed examination
 • Learn how methods and limitations are written in practice
 • See discipline-specific writing styles, not generic templates
 • Identify gaps and angles that published papers often hide
 • Reduce anxiety by realising: your draft doesn’t have to be perfect to be valid

PhDs are not written by magic. They are written by patterns you can learn.

PS: Which thesis repositories have helped you most during your PhD journey?

Share in the comments.

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What do you check first in a paper?

Papers with clear charts and diagrams get read more, understood faster, and remembered longer.

Why?
Because reviewers and readers don’t read line by line.

They scan.

Figures act as cognitive anchors:
 ✅ They summarise complex arguments at a glance
 ✅ They guide the reader through results
 ✅ They signal clarity, rigour, and confidence
 ✅ They reduce mental effort for busy reviewers

A strong figure can do what three paragraphs struggle to explain.

That’s why well-designed diagrams often get cited, reused in talks, and remembered while text-heavy papers get skimmed and forgotten.

This doesn’t mean “add more charts.”

 It means add purposeful visuals that carry the argument forward.

If your key finding cannot be visualised clearly, it is often not clear enough yet.

📌 Tools for data visualisation (charts, graphs, dashboards)
Tableau – Interactive dashboards; widely used in research and policy settings
Microsoft Power BI – Strong for structured data and reporting
GraphPad Prism – A staple in biomedical and experimental research
R (ggplot2) – Publication-quality figures with full analytical control
Python (Matplotlib / Seaborn) – Flexible, reproducible scientific visualisation

📌 Tools for scientific illustrations & diagrams (conceptual figures, workflows)
BioRender – Gold standard for life-science figures
Mind the Graph – Accessible visuals for papers and presentations
Adobe Illustrator – High-end, journal-ready figure design
Inkscape – Free alternative for scalable diagrams
PowerPoint – Still one of the most used tools for assembling publication figures

PS: What more tools would you add?
Share in the comments

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Follow Dr Priya Singh, Founder Research Made Clear for morte academic writing tips.

If your research gap is weak, your whole study stands on shaky ground.

Finding a real research gap is about seeing what is missing, unclear, or unresolved in what we already know.

Here are some Do’s and Don’ts to help you get it right 👇
✅ DO’s When Identifying a Research Gap

1. Do read for patterns, not just papers

Don’t treat articles like isolated pieces. 
Look for:
✅Repeated limitations
✅Conflicting findings
✅“Future research should…” statements
 Patterns point to gaps.

2. Do map what is known vs. unknown
Make two columns:
✅What researchers agree on
❓What is still unclear, under-tested, or debated
 Gaps live in the second column.

3. Do narrow the context
A gap becomes strong when it is specific:
✅A certain population
✅A setting or country
✅A method not yet used
✅A variable not yet examined together with others
Broad gaps are weak. Focused gaps are publishable.

4. Do check recency
A gap from 2012 may already be filled. Always check the latest 3–5 years of literature before finalizing your gap.

5. Do connect the gap to a real problem
Editors and reviewers love gaps that matter.
 
Ask: Who is affected if this gap remains unaddressed?

❌ DON’Ts That Weaken Your Research Gap

1. Instead of “No one has studied this”
 This is rarely true and reviewers will prove it in seconds.
 Instead say:
 👉 “Limited research has examined…”
 👉 “Findings remain inconsistent regarding…”

2. Don’t confuse a topic with a gap
“Social media and mental health” is a topic.
 
A gap sounds like:
“Few longitudinal studies have examined how social media use predicts anxiety among adolescents in low-income settings.”

3. Don’t copy the ‘future research’ line blindly
Use it as a clue, not as your gap. You still need to justify why that missing piece is important.

4. Don’t make the gap too big
You are not solving the entire field’s problem in one PhD.
A good gap is manageable, specific, and researchable.

5. Don’t write the gap before the review is mature
If you decide your gap too early, you’ll force the literature to fit your idea instead of letting the evidence guide you.

PS: Are you studying a topic… or solving a clearly defined gap?
Share in the comments

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PowerPoint feels clumsy and time-consuming

That is where graphical abstracts come in.

A graphical abstract is a single visual snapshot of your study:
the question, the method, and the key takeaway, all at a glance.

Why do Graphical abstracts matter today?
·      Editors use them to quickly grasp relevance
·      Reviewers use them to understand logic and flow
·      Readers use them to decide whether to click, read, or cite
·      Journals use them to increase visibility and sharing

In short, A good graphical abstract earns your paper attention.
Yet this is where many researchers struggle.

Recently, one of my mentees asked me for a practical tool to create a graphical abstract quickly, without design stress.

That’s why, I am sharing a demo showing how I used AnswerThis (YC F25) to break a study into visual components, convert methods and findings into clean diagram.

Step 1: Go to https://lnkd.in/ea5SUYVg and open the Diagrams feature (or select Diagram from the creation options).

Step 2: Select Diagram, then tell the tool what you want to create.
In my case, I wanted a graphical abstract.

Step 3: Paste your study abstract + add a clear instruction
I copied the study’s abstract and used a prompt like this:
“Create a graphical abstract for this study. Use the abstract below to summarise the background, aim, methods, key findings, and conclusion in a clean, journal-style layout. Abstract: [paste abstract]”

Step 4: Run the prompt
Click the red up arrow / Run button to generate the diagram.

Step 5: Review what the tool generates
Within a few minutes, the tool produces a graphical abstract and typically provides:
a short overview/explanation of the visual structure
how it translated the abstract into sections (aim, methods, findings, takeaway)

Step 6: Export the graphic
The output is available as a high-quality PNG. You can:
use it directly, or
use it as a base draft and refine labels/layout in your preferred design tool.

Step 7: Accuracy check (non-negotiable)
Before using it in a submission or posting it online:
verify every label, number, direction of effect, and conclusion
ensure the visual matches the paper (not assumptions or “typical” logic)

The key point here is not the tool itself, but what AI now enables.

AnswerThis (YC F25) can help researchers:
·      translate dense text into visual logic
·      reduce cognitive load during design
·      focus on clarity, not cosmetics
·      save hours without compromising accuracy

Graphical abstracts are no longer nice to have.
 
They are part of modern scholarly communication in many fields.

PS: What tools are you using to create graphical abstracts or research visuals?
Share in the comments

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Data speaks in results. You speak in the discussion.

The discussion is where you prove you understand your results.

Here’s a simple way to write a strong discussion section 👇

1. Start with the big answer, not the details
 Open by answering your research question in plain language.
 What did you actually find?
 
2. Explain the “why,” not just the “what”
 Results tell what happened.
 Discussion explains why it matters.
 
Ask yourself:
 “Why did this result turn out this way?”
 “What does this tell us about the problem?”

3. Connect your findings to existing studies
 Don’t list papers.
 Compare ideas.

 Say whether your findings:
 – support previous work
 – challenge it
 – extend it to a new context

4. Be honest about limitations
 Every study has limits.
 
Name them calmly and briefly.
Then explain why your findings are still useful despite those limits.

5. End with implications, not repetition
 So what?
 
What should researchers, practitioners, or policymakers do differently now?
This is where your study earns its value.

Strong discussions don’t restate data.
They interpret, connect, and position it.

PS: Which part of the discussion do you find hardest to write?
Share in the comments.

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Follow Dr Priya Singh for more research writing guides 

Founder, Research Made Clear

There was a phase in my own PhD where I didn’t feel “stuck”.

I was busy.

Reading more. Tweaking things. Making notes. Starting and restarting documents.
 
Telling myself, I’m working… just not ready yet.

Weeks passed. Then months.
Nothing was technically wrong.
 
But I had quietly lost the thread.

What I see now, after mentoring so many PhDs is that this happens to almost everyone.

Because a PhD is long enough for you to forget what you’re actually trying to finish.

You stay productive, but directionless.
You mistake movement for progress.

Here’s what actually helps:

✅ Decide what is in scope and what is not. Write it down. Stop renegotiating it daily.
✅ Decide on a weekly finish line (a section, a figure, 1,500 words, not “work on Chapter 3”).
✅ Decide when something is good enough to send, not perfect enough to keep.
✅ Decide that unclear feedback means act and clarify later, not pause indefinitely.

Finish one small thing this week.
Momentum follows decisions.

That’s how PhDs get finished.

PS: What are you finishing next—section, figure, or analysis?
Share in the comments.

REPOST for every PhD who needs to know this,

Follow Dr Priya Singh, Founder Research Made Clear for PhD survival tips.

Ten simple Rules for finishing your PhD

❓Three terms. One chapter. Endless confusion 🤔 

Literature review, theoretical framework, and conceptual framework do not mean the same thing. 

And confusing them can weaken your entire study.

Here is the difference in simple terms:
1️⃣ Literature Review = What we already know
This is where you show the research landscape.

You summarize, compare, and critique previous studies to explain:
✅ What has been studied
✅ What findings exist
✅ Where the gaps are

Example:
If your topic is AI use in medical education, your literature review might discuss:
Studies on AI tools for diagnosis training
Research on student attitudes toward AI
Evidence on learning outcomes

2️⃣ Theoretical Framework = The theory you stand on
This is about existing theory that explains why things happen.

✅ You choose a well-established theory that helps interpret your study.

Example:
If you use Technology Acceptance Model (TAM), you might say:
Students’ perceived usefulness of AI influences their intention to use it
Perceived ease of use affects adoption

Here, the theory provides a lens. You are saying:
 “I am viewing my study through this theory.”

3️⃣ Conceptual Framework = Your study’s blueprint
This is your model, built for your specific research question.
 
It may be inspired by theory, but it is customized.

You show:
✅ Your key variables
✅ How you believe they are related

Example:
You may propose:
AI training → AI confidence → AI usage in clinical learning
And you may add:
Faculty support strengthens this relationship

That diagram? That’s your conceptual framework.

When you separate them clearly:
✅ Your literature review justifies the gap
✅ Your theoretical framework explains the logic
✅ Your conceptual framework shows your research design

PS:  Are you describing what others found… or explaining how your variables connect?

Share in the comment.

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A systematic review without a plan is just academic burnout in slow motion.

A PhD student once told me, “I thought a systematic review would take 3 months. It’s been more… and I’m still screening papers.”

That frustration is more common than people admit.

Systematic reviews and meta-analyses look straightforward from the outside. 
Search some databases, read papers, summarize results. 

But in reality, they can quietly drain months (sometimes years), and in my opinion, most delays happen because the process is not structured from the start.

A strong review involves designing the journey, before you begin walking.

Here’s what saves time, stress, and rework:
✅ Be brutally clear about your research question before you search
 A vague question = endless screening and confusion later.
✅ Decide your rules early
Inclusion, exclusion, outcomes, study types. If you define these late, you’ll keep second-guessing every paper.
✅ Work in pairs, not alone
Independent screening and extraction don’t just reduce bias. They reduce mistakes that cost weeks to fix.
✅ Treat data like a database.
If extraction isn’t structured, analysis becomes a nightmare.
✅ Don’t assume you must do a meta-analysis
Sometimes the most honest and useful review is narrative. Forcing numbers where they don’t belong weakens your study.
✅ Document as you go
Trying to rebuild your flow diagram and methods section at the end is one of the biggest time traps in evidence synthesis.

A well-planned systematic review doesn’t just produce stronger evidence.
 It protects your time, your energy, and your sanity.

PS: Did your review take much longer than you expected?
Share in the comments

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The Discussion section is where good papers either land… or lose reviewers.

A strong Discussion is not a summary.
It’s your argument.

✅ Start with the answer, not the data
Open by clearly stating what your findings mean, not what you found. The reader already knows the results.

✅ Anchor every claim to evidence
If you say something is “important” or “novel,” explain why and link it back to theory or prior studies.

✅ Compare, don’t compete
Show how your findings align with, extend, or challenge existing work. You’re joining a conversation, not trying to win it.

✅ Explain surprises honestly
 Unexpected results are not weaknesses if you interpret them thoughtfully.

✅ Separate interpretation from implication
First explain what the results mean. Then discuss why they matter for practice, policy, or future research.

✅ End with contribution, not repetition
Your final paragraph should answer one question:
What does the field know now that it didn’t know before this study?

If Results show what happened,
Discussion explains why it matters.
That distinction changes everything.

PS: What would you add?
Share in the comments.

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How we write a manuscript discussion

Last year, a mentee of mine almost gave up on her PhD dream!!

She had strong grades. 
A meaningful topic. 
Real motivation.

But after multiple university rejections, she was exhausted.

What hurt most wasn’t just the “no.”
It was the time.

Months spent drafting.
Weeks waiting for decisions.
Emotional energy poured into applications that quietly went nowhere.

When we finally sat down to review her research proposal, the issue became clear very quickly:

Her idea made sense in her head.
But on paper, it didn’t yet look like a clear, doable research plan.

And that’s where many applicants struggle.

Universities aren’t only asking,
“Is this topic interesting?”

They’re asking:
Do we clearly understand the problem?
Are the questions focused enough to guide a study?
Is the method realistic for a PhD timeline?
Does this project show a logical path from idea to outcome?

Once we reworked her proposal with those questions in mind, everything changed.

Not her topic.
Not her passion.
 
Just the clarity, structure, and flow of her proposal.
In the next application round, she received interview invitations and an offer.

That experience is exactly why I created this guide on crafting a strong research proposal. 👇 

Because a proposal is a document that helps reviewers quickly see:
✅ what you want to study
✅ why it matters
✅ how you will actually do it
✅ and whether the project is feasible

A good proposal doesn’t try to sound impressive.
It makes the research easy to understand and easy to trust.

If you’re planning to apply for a PhD or research program, don’t wait for another rejection to realise the proposal might be the missing piece.

PS: What part of writing a research proposal feels hardest for you right now?
Share in the comments.

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Follow Dr Priya Singh, Founder Research Made Clear for more research tips.

A PhD is rarely finished by inspiration. It’s finished by routine.

Writing a PhD thesis is a process.

After years of supervising and examining theses, here’s the simple truth most students aren’t told:

1. Start with structure before sentences
Do not begin by writing chapters.

Begin by locking:
Your research question
Your chapter map
What each chapter must prove

If you can explain your thesis structure on one page, writing becomes execution, not confusion.

2. Write to answer questions, not to sound academic
Every section exists to answer one question:

Introduction → What problem are you solving and why now?
Literature review → What do we already know, and where is the gap?
Methods → Why was this the right way to study it?
Results → What did you find?
Discussion → What does it mean?


3. Separate “thinking” from “writing”
Most PhDs get stuck because they try to think and write at the same time.

Instead:
Think first (notes, bullet points, rough logic)
Write later (clean sentences, flow, references)
This removes perfectionism from the process.

4. Write small, consistently
You do not need long, perfect writing days.

You need:
30–45 minutes
One clear task
One subsection at a time

Consistency beats intensity every single time.

5. Expect ugly drafts
A strong thesis always starts messy.

Your first draft is not evidence of your intelligence.
It is raw material.

Clarity comes from revision, not from waiting to feel “ready.”

6. Treat feedback as data, not judgment
Supervisors are not reading to admire your work.
They are reading to stress-test it.

When feedback comes:
Categorise it (structure, clarity, theory, method)
Address patterns, not emotions
Revise strategically, not defensively

This is how strong theses are built.

PS: Are you writing regularly, or only when deadlines panic you into it?
Share in the comments.

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Follow Dr Priya Singh, Founder Research Made Clear for more PhD writing tips.

The Method section is not a formality.
 It is where reviewers decide whether to trust your study.

Here’s how to use it properly when writing 👇

1. Research Design
Don’t just name the design.
 
Briefly explain why this design was the right choice for your research question.

2. Participants / Sample
Be explicit about:
Who was included (and excluded)
How participants were selected
Ethical approval and informed consent
If there are sampling limits or constraints, name them calmly. 

3. Variables / Constructs
Clarity matters more than complexity.

Quantitative: define independent, dependent, and control variables clearly
Qualitative: explain key concepts, themes, or guiding questions

If readers can’t tell what you actually studied, the analysis won’t matter.

4. Data Analysis
Explain how you made sense of the data, step by step.
What analysis approach did you use?
Why was it appropriate?
What software supported the process?

5️⃣ Limitations (Optional, but powerful)
Strong papers don’t hide limitations.
They frame them responsibly.

Briefly note:
Sample constraints
Potential biases
Limits to generalisation

Other details that reviewers quietly appreciate
Software and tools used
Study timeline
Ethics approval reference number

Small details = big trust signals.

📌 Save this infographic for your next paper.

PS: What would you add? 

Follow Dr. Priya Singh for practical research writing guidance.

Most thesis stress comes from unclear expectations‼️ 

One of the fastest ways to feel confident and finish is to understand where you are in the thesis journey and what your stage is actually trying to train you to do.

Here are few things I advice my mentees: 
✅ Ask “What does success look like for this thesis?” In your first supervision meeting, request 2–3 sample theses/dissertations your department considers “good.” 
✅ Treat your thesis like a project, not a mood. Put milestones on a calendar: proposal, ethics, data collection, analysis, chapter drafts, revisions. 
✅ Create a “decision log.” Every time you choose a method, sampling approach, exclusion rule, or framework—write a 2–3 line justification. This saves you during viva/defense and revisions.
✅ Write earlier than you feel ready. Don’t wait for “complete reading.” Start with rough chapter skeletons and expand weekly. Writing is how thinking gets clearer.
✅ Use chapter checklists. Each chapter needs a purpose, a through-line, and a final “so what.” If you can’t summarise a chapter in one sentence, it’s still fuzzy.
✅ Separate reading into two lists: “Must-cite” (core, directly relevant) and “Nice-to-know” (interesting but optional). This prevents literature-review overload.
✅ Build a weekly evidence habit. Every week produce something tangible: a table, a figure, coded notes, an analysis memo, 500–800 words. 
✅ Get alignment on feedback style. Ask your supervisor: “Do you prefer full-chapter drafts or smaller sections?” and “Should I submit polished writing or rough drafts?” 
✅ Stop comparing timelines. Different disciplines and data types move at different speeds. Compare your progress to your plan, not someone else’s chapter count.
✅ Protect your “deep work” slots. Two focused sessions a week (even 60–90 minutes) consistently beats weekend panic-writing.

PS: Where are you right now in your thesis journey and what’s the one thing you wish someone had explained earlier?

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