Angela Priest

I'm a data geek and process nerd who loves to build people up and help them grow. For 20 years, I've been turning chaos into order at companies across all stages of growth to ensure that their teams flourish. I'm now throwing my passion for helping people into helping PhDs, PostDocs, and Mid-Career Researchers get jobs in industry. My goal is simple: to help brilliant minds thrive outside the academic world and make real impact in their new careers.

Check out my website to learn more: www.alma.me

Co-Founder and COO

At Alma.Me, we help PhDs kick start their careers in industry through on-demand courses, online communities, networking, and resources to help them make the transition from academia to industry with confidence.

Dear Employers: Please stop listing “5+ years of industry experience” when what you actually need is 5 years of APPLIED experience.

A PhD who spent 6 years designing experiments, analyzing complex datasets, and presenting findings to skeptical committees has that experience.

They just didn’t do it inside your building.

The work is real. The skills transfer. The titles are the only thing missing.

Please write job descriptions for what you actually need, not for a vague (and often meaningless) criteria.

#phd #hiring #careers #humanresources

PhD Career Guide | Hiring Manager | 20+ Yrs Building High Performing Teams | COO @ Alma.Me

“I feel overqualified AND underqualified at the same time.”

If you’re a PhD in the job market, you’ve probably felt this.

Too experienced for entry-level. Not enough “industry experience” for mid-level. It feels like there’s no door with your name on it.

But here's how you can reframe it:

“Industry experience” on a job posting is a proxy. It’s shorthand for “can this person do the work and communicate with our team.”

You can. You just need to show it in language they recognize.

Translate your research into business outcomes. 

Frame your collaboration as cross-functional teamwork. 

Connect the dots between what you’ve done and what they need.

The gap isn’t in your skills. It’s in how they’re being presented.

👉 Are you a PhD struggling to land a non-academic job? Check out the job search program on our website: www.alma.me

We accept 2 new PhDs per week.

#phd #jobsearch #careers #stemcareers

PhDs: Here's how to write resume bullet points that actually get you interviews.

Most resume bullets I see from PhDs describe what they did:

"Conducted analysis of gene expression data."

That tells me the activity. It doesn't tell me the impact.

Here's a simple framework that works:

Action + Method + Result

"Developed a gene expression analysis pipeline (R, Python) that reduced data processing time by 40%, accelerating publication timelines for 3 concurrent studies."

Same work. Completely different signal.

A few rules:

1️⃣ Start with a strong action verb: "Developed," "Designed," "Led," "Built" 
2️⃣ Include the method, tool, or skill you used 
3️⃣ End with the outcome. Quantify if possible.
I
f you can't quantify, state the benefit: "...enabling faster decision-making for the research team."

Keep each bullet under 2 lines. If it takes more, you're probably including too much detail.

Leave room for the interviewer to be curious. That's what gets you into the room.

🔄 Pass it along - repost to help a friend :)

👉 Want help translating your academic work into industry-ready bullet points? Check out the job search program on our website: www.alma.me

We accept 2 new PhDs per week.

#phd #jobsearch #careers #humanresources

Having looked at hundreds of resumes from PhDs - the same problem comes up over and over.

The resume reads like an academic CV.

Pages of methods, publications, techniques.

And very little about what they can actually deliver for a business.

Here's the thing - the experience is there every. single. time.
Every single time.

PhDs just haven't been taught how to communicate it in a way a hiring manager understands.

That's the gap we close every day at Alma.Me

And it's exactly what I'm covering in my free workshop as part of Proteintech's Careers Week 2026:

🟣 What hiring managers actually look for on a resume
🟣 How to translate your research into business language
🟣 How to write bullet points that show impact - not just activity

📅 Tuesday March 24
🕘 9 am CST / 2 pm GMT / 3 pm CEST

Free. Live. Practical.

Register here: https://lnkd.in/dkiPUimU

🔄 Pass it along - repost to help a PhD friend

I help PhDs, Postdocs & Lecturers secure tenure-track jobs and navigate academia | Insider knowledge tips on how to succeed | Academic Career Mentor | Professor & Research Director

I was a lost PhD student. Confused. Overwhelmed. Ready to quit.

Sitting in my tiny office at 2 AM, drowning in research papers, I made a promise...

"If I somehow make it through this, I'll help others navigate what I'm struggling with."

Today, I'm a Professor at a top UK university.

But here's what matters more...

I've kept that promise by helping researchers complete their PhDs and navigate academia.

------

The brutal truth about doing a PhD/DBA whilst working

I hear the same struggles from the 26% of PhD & DBA students who are working professionals:

→ I can't keep up -the mental capacity is exhausted
→ I'm juggling three full-time jobs with no bandwidth
→ I've been stuck for 3-6 months with no real progress
→ My family thinks my PhD/DBA is a hobby, not a second job
→ I feel isolated - no one understands what I'm going through

Sound familiar? You're not alone. And it's not your fault.

Universities train PhD/DBA researchers for academia, but working professionals need something completely different:

❌ You're investing $25-80K but risk joining the 33% who drop out
❌ Motivation crashes after major life events (births, job changes, pandemic)
❌ Distance learning leaves you isolated, struggling to navigate systems alone
❌ You don't have 40 hours/week; you have 10 hours scattered across nights & weekends  
❌ Your supervisor gives great academic feedback but doesn't understand work-life-PhD balance

That's why I created specialised services for serious researchers.

My transformation:

→ 30+ PhDs/DBAs mentored to success
→ 130+ publications across top journals
→ £9 million in research funding secured
→ From PhD uncertainty to Professor by 40
→ 230K researchers follow my guidance daily
→ Research reached 30+ million people globally

FREE RESOURCES:

✅ Weekly newsletter (10K+ subscribers) www.phdtoprof.com/newsletter
✅ Free academic resources www.phdtoprof.com/store
✅ AI tools for ethical research productivity

PREMIUM SERVICES:

🎯 Premium PhD/DBA Mentoring - For senior professionals completing their PhD/DBA on time
🎯 PhD/DBA Proposal Development - Support developing and passing proposals
🎯 Expert Thesis Review - Comprehensive dissertation feedback from an experienced PhD examiner
🎯 AI in Research Masterclasses - Responsible and ethical AI use for researchers
🎯 University & Institution Training - Workshops on responsible AI in research for academic teams

My mission:

Help you complete your PhD/DBA on time while using AI responsibly in your research.

Academic success doesn't have to be a mystery. You just need the right guide.

Emmanuel Tsekleves

I've examined 45+ PhD theses. I ask the same 10 questions every time. Most candidates stumble on question 3. Here's what they are and how to answer each one.

Last year, a strong candidate walked into her viva with a thesis I genuinely admired. Solid methodology. Clear contribution. Well written.

Then I asked question 3. She froze.

This happens more often than you think. 
Not because candidates are unprepared. 
Because nobody tells them what examiners are actually testing.

Here are the 10 questions I ask in every examination.

1. "Can you summarise your thesis in 3 sentences?" 
I'm testing clarity of thought. 
If you can't distil your work, you don't fully understand it yet.

2. "What is the original contribution of your research?"
I'm testing whether you know what makes your work matter. 
Many candidates undersell this.

3. "Why this methodology and not an alternative?" 
This is where most fail. 
They chose their methodology because their supervisor told them to. 
Not because they understood why. 
Examiners notice immediately.

4. "What are the limitations of your study?" 
I'm testing intellectual honesty. 
If you say "none," I worry.

5. "How does your work relate to [specific paper in your field]?" 
I'm testing depth of literature knowledge. 
I pick a paper from your reference list. 
If you can't discuss it, I know you haven't read it properly.

6. "If you could start again, what would you do differently?" 
I'm testing reflective capacity. 
The best answers show growth, not regret.

7. "How would you respond to [specific critique of your approach]?" 
I'm testing your ability to defend under pressure. 
Stay calm. Acknowledge the point. 
Then explain your reasoning.

8. "What are the practical implications of your findings?" 
I'm testing real-world relevance. 
If your research has no application beyond the thesis, that's a problem.

9. "How does your theoretical framework connect to your data analysis?" 
I'm testing coherence. 
The strongest theses have a clear thread from philosophy to findings.

10. "What should be the next study after yours?" 
I'm testing forward thinking. 
If you can't answer this, you haven't thought beyond your own submission.

Question 3 catches the most candidates because choosing a methodology is the single most important decision in your PhD. 
And most students never learn to justify it.

Which question would catch you off guard?

Save for your viva prep.

Repost to help a PhD student preparing for their defence.

#PhDViva #PhDExamination

I've created a full viva preparation guide with model answers for each of these 10 questions. Available in my newsletter this week. Register for free to access it: https://lnkd.in/ezkxz2m2

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Writing Guides

Ready for more? 

Join my exclusive weekly Academic Pathfinder newsletter where I share the exact strategies that have helped hundreds of PhDs finish their PhDs on time, secure competitive tenure-track positions and thrive in academia. 

Get Wednesday's insider tactics delivered straight to your inbox: https://phdtoprof.com/newsletter

Newsletter

Ready for more? 

Join my exclusive weekly PhD Insider newsletter where I share the exact strategies that have helped hundreds of PhDs finish their PhDs on time, use AI responsibly in Research and thrive in academia. 

Get Wednesday's insider tactics delivered straight to your inbox: https://phdtoprof.com/newsletter

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👉 Try for free: https://www.bohrium.com/?utm_source=oversea&utm_term=emmanuel&utm_campaign=kol

PhD 1:1 Autoplug 1 (Form-directed)

Doing your PhD while working full-time is lonely.

Your supervisor is juggling 12+ students. They don't understand how it feels to squeeze research into exhausted evenings and weekends. You get vague feedback like "needs more depth" with no clear direction. Months pass. You start wondering if you'll ever finish.

I've mentored 30+ mid to senior professionals through this exact struggle to successful completion.

20 years at a top 1% university. 25+ PhD examinations. I know where you're losing time.

I take on a small number of dedicated professionals for private premium 1:1 mentorship.

Interested? Start here: https://forms.gle/L3wuDxfAgvDA17SD8

1:1 mentoring Autoplug 2 (Form-directed)

Ready for more? 

Join my exclusive weekly Academic Pathfinder newsletter where I share the exact strategies that have helped hundreds of PhDs finish their PhDs on time, use AI responsibly in Research and thrive in academia. 

Get Wednesday's insider tactics delivered straight to your inbox: https://phdtoprof.com/newsletter

1:1 Mentoring Autoplug 3 (DM-directed)

You're trying to do three jobs at once: career, family, PhD.

Your supervisor has never lived that reality. I have spent 20 years mentoring professionals who have.

Private premium 1:1 mentorship for mid to senior professionals serious about finishing. Limited availability.

Interested? Start here: www.phdtoprof.com


1:1 Mentoring Autoplug 4 (DM-directed, shortest)

Feeling behind on your PhD? Supervisor too busy to help? Wondering if you'll ever finish?

I provide private premium mentorship for mid to senior working professionals. 1:1 support. Limited spots.

DM "MENTORSHIP" with your situation.


Proposal development

Writing your upgrade proposal while working full-time is a different kind of hard.

Your supervisor is juggling 12+ students. They don't understand how it feels to squeeze proposal writing into exhausted evenings and weekends.

You get vague feedback like "needs more depth" with no clear direction.

Your upgrade deadline approaches. You start wondering if you'll even pass.

I've mentored 30+ mid to senior professionals through this exact struggle to successful completion.

20 years at a top 1% university. 40+ PhD examinations. I know where you're losing time.

I take on a small number of dedicated professionals for private premium 1:1 mentorship.

Interested? Start here: www.phdtoprof.com

Academic Writing

Writing your PhD while working full-time makes this ten times harder.

Your supervisor gives feedback like "needs better flow" with no clear direction.

You rewrite the same paragraph five times after a 10-hour workday.

You wonder if your English is good enough. If your arguments are strong enough. If you'll ever get published.

Frameworks and free guides help. But they can't give you personalised feedback on your specific writing.

I've mentored 30+ mid to senior professionals through this exact struggle to successful completion.

20 years at a top 1% university. 40+ PhD examinations. 130+ publications.

I know where your writing is losing marks. And I know how to fix it fast.

I take on a small number of dedicated professionals for private premium 1:1 mentorship.

Interested? Start here: https://forms.gle/L3wuDxfAgvDA17SD8

1:1 Mentoring

Choosing between an MBA and a DBA is one of the biggest decisions a working professional can make.
 
The wrong choice costs you 3 to 5 years and tens of thousands of pounds.
 
I've supervised both. I know which path fits which person.
 
I take on a small number of dedicated professionals for private premium 1:1 mentorship. From choosing the right programme to completing your doctorate on time.
 
Interested? Start here: https://www.phdtoprof.com

Lead magnet

If you're struggling with your PhD right now, I've put together the exact milestone planning template I used with this student. Link in my newsletter

Store

I've expanded this into a full downloadable guide with annotated examples for each level of the funnel.

You can get it from here:  https://phdtoprof.com/store

If you found this framework useful, I've created a library of free and paid guides for every stage of your PhD.

Literature review templates, thesis chapter outlines, methodology decision trees, and more.

Browse the full collection here: https://phdtoprof.com/store


Newsletter 2

Posts like this one scratch the surface. Every Wednesday I go deeper.

One insider technique to complete your PhD on time, sharpen your research skills, and use AI responsibly. In under 4 minutes.

Join 10,000+ researchers getting my free PhD Insider newsletter: https://phdtoprof.com/newsletter

Resources

Here are the links to all 8 courses:

Understanding Research Methods, University of London: www.coursera.org/learn/research-methods
Qualitative Research Fundamentals, Yale University: www.youtube.com/watch?v=wbdN_sLWl88
Writing in the Sciences, Stanford University: www.coursera.org/learn/sciwrite
Introduction to Systematic Review, Johns Hopkins University: www.coursera.org/learn/systematic-review
How to Write and Publish a Scientific Paper, École Polytechnique: www.coursera.org/learn/how-to-write-a-scientific-paper
Research for Impact, University of Cape Town: www.coursera.org/learn/research-for-impact
Persuasive Writing and Public Speaking, Harvard University: pll.harvard.edu/course/rhetoric-art-persuasive-writing-and-public-speaking
Data Analysis: Visualisations in Excel, The Open University: www.open.edu/openlearn/science-maths-technology/data-analysis-visualisations-excel/

For more curated resources like this, join 10,000+ researchers in the free PhD Insider newsletter: www.phdtoprof.com/newsletter

I help working professionals complete their PhD/DBA/EDBA on time | Professor | 45+ PhD Theses Examined | Follow for posts on Research Skills, AI in Research & Academic Career Success

Most PhD supervisors will never tell you this. The real reason PhD students fail.
It is not methodology. It is not data. It's...

It is that they never clearly identified a research gap

I had a student last year who spent 8 months reading
But when I asked her one question, she went silent

"What is actually missing from this literature?"

She could tell me everything researchers had found
But she could not tell me what they had missed

This is the most common problem I see
Students describe the research
They never identify the gap

So I started teaching the 7 types of research gaps:

1. Evidence Gap
No study has investigated this topic, population, or context at all.
The simplest gap. The literature is simply silent.

Where to start: Search for your topic plus a specific population or setting. If nothing comes up, you may have found an evidence gap.

2. Knowledge Gap
Studies exist, but they have not explained why something happens.
We know the what. We do not know the why.

Where to start: Look for studies that report findings but call for further investigation into the underlying mechanisms.

3. Practical Gap
Research exists in theory but has not been applied or tested in practice. 
The gap sits between what we know and what we do.

Where to start: Compare academic literature with industry reports or professional guidelines. Where do they disconnect?

4. Methodological Gap
The topic has been studied, but only using one approach.
A different method could reveal different insights.

Where to start: If every study in your area uses surveys, there may be a methodological gap for qualitative or experimental approaches.

5. Theoretical Gap
No existing theory adequately explains the phenomenon.
Current frameworks do not fit the data.

Where to start: Look for studies where researchers say their findings were "unexpected" or "inconsistent with existing theory."

6. Empirical Gap
A theory exists but has never been tested with real data, or has only been tested in one context.

Where to start: Find well-cited theoretical papers and check whether they have been empirically validated in your context.

7. Population Gap
Research exists but only covers certain groups, regions, or industries.
Your population has been excluded.

Where to start: Check the sample descriptions in key studies. If they all use the same population, every other group is a potential gap.

The secret most supervisors do not teach: the strongest research questions combine two or more gap types.

Evidence gap plus population gap: "No study has examined X among Y."

Methodological gap plus knowledge gap: "Using interviews instead of surveys to understand why Z happens."

One gap gives you a research question.
Two gaps give you an original contribution.

Which gap type fits your research area best?

Want the high-res PDF poster? Subscribe to my newsletter.

Save this for when you write your literature review.
Repost for a colleague still searching for their topic.

#ResearchGaps #PhDTopic

How I taught my PhD students to turn boring abstracts into reader magnets.
One of my mentees had submitted to 4 conferences before we started working together. Rejected from all 4. Here's how we fixed it: 

His research was solid. His methods were rigorous. His results were genuinely interesting.

But his abstract read like a methods section. Dense. Flat. Forgettable.

In our first session, I restructured his abstract using the same 6-step framework I teach every researcher I mentor.

Same research. Same results. Different structure.

He resubmitted to a conference the following month.

Accepted first time.

The framework is simple:

1) Start with relevance. Make the reader care in the first sentence.

2) Define the gap. Show why your study is necessary.

3) Clarify your aims. Leave no doubt about your direction.

4) Summarise your approach. Establish credibility in one line.

5) Highlight your discoveries. Lead with your most important result.

6) Explain the impact. End with why it matters.

Most abstracts fail because researchers try to summarise everything.

Strong abstracts do the opposite. They make reviewers curious enough to read the full paper.

His research didn't change. The way he communicated it did.

Every sentence in an abstract must justify its place.

I've broken down each step in detail in the visual below.

---

Which part of abstract writing do you find most challenging?

#AcademicWriting #PhDLife

I tested 12 AI writing tools over 3 months.
Here's which ones actually pass journal editorial standards.

A talented PhD student came to me in panic recently.

His manuscript was desk-rejected.

Not for weak science. For using an AI tool the wrong way.

The editor's message was blunt: "Evidence of undisclosed AI-generated content. Submission withdrawn."

One tool. One mistake. Months of research thrown away.

This is happening more than you think.

Journal editors are vigilant. They're testing submissions. They're catching mistakes.

After months of conversations with editors and tracking submission outcomes, I've identified a clear pattern:

Some AI tools are safe when disclosed properly.

Others will get your paper rejected instantly.

The difference comes down to one question:

Does the tool create content or does it check and organise your content?

Tools that check grammar, improve clarity, refine translations, or help you structure your arguments are generally accepted.

Tools that generate text, draft paragraphs, or produce content you pass off as your own will end your publishing career.

I've mapped all 12 tools I tested into two clear categories in the visual below.

Green means editors accept it with proper disclosure. Red means it puts your career at risk.

The rule I teach every researcher I mentor:

If AI creates content, always disclose it.
If it checks or organises, you're generally safe.
When uncertain, err on the side of caution.

No shortcut is worth your reputation.

Academic integrity is non-negotiable.

---

Which AI tool have you found most useful in your writing process?

#AIinResearch #AcademicWriting

My first 5 grant applications were rejected. Every single one. 
Here's how I went from £10k to £10m in research grant funding:

I remember opening that fifth rejection email and thinking maybe my research just wasn't good enough. 
Maybe I wasn't cut out for this.

Then a panel reviewer told me something that changed everything. 
She said: "I stopped reading on page 2."

Not because the science was weak. 
Because the way I presented it was.

I had buried the real-world impact on page 3. 
I led with the literature gap instead of the problem. 
My methodology was sound but my narrative was invisible.

I was writing for academics. I should have been writing for funders.

So I rebuilt my entire proposal structure around three principles. 

I now call it the 3P Proposal Structure.

P1: Problem Framing. Lead with the real-world problem and its cost. 
Not the gap in the literature. 
Funders don't fund gaps. 
They fund solutions. 
"This problem costs the NHS £2.3 billion annually" hits harder than "this area remains under-explored."

P2: Path Innovation. 
Show what you will do differently. 
Not just what you will study. 
Every applicant studies something. 
Very few explain why their approach is the one that will actually work.

P3: Projected Impact. 
Connect your outcomes to the stakeholders who fund research. 
If the funder can see themselves in your story, you win.

Same research question. 
Completely different proposal structure. 
The next application secured half a million pounds.

Then a million. Then over the course of my career, more than £10 million in research funding.

Grant writing is storytelling. 
Your research is the plot. 
The funder needs to see themselves in the story.

What's the most frustrating feedback you've received on a grant application?

Save this framework.

Repost for anyone applying for funding.

#GrantWriting #AcademicFunding

I have reviewed hundreds of free online courses for PhD researchers. Most are not worth your time. These 8 are the ones I give every student I supervise.

No paywalls. No certificates required. Just the courses that actually make a difference.

A quick note: courses on Coursera can be audited free. Full content, no certificate. Click "audit this course" when you enrol.

Here they are.

1. Understanding Research Methods
University of London, Coursera

The clearest foundation course I've found. Covers the full methodology chain from philosophy to methods. If you're confused about how the pieces connect, start here.

2. Qualitative Research Fundamentals
Yale University, YouTube

A rigorous introduction to qualitative research from one of the world's leading universities. Free on YouTube. Essential if your study is interview or observation-based.

3. Writing in the Sciences
Stanford University, Coursera

The best academic writing course available free online. Works for any discipline despite the name. I recommend this to every student who tells me their writing feels unclear.

4. Introduction to Systematic Review and Meta-Analysis
Johns Hopkins University, Coursera

Essential for anyone doing a literature-heavy thesis. Covers the full process from search strategy to synthesis.

5. How to Write and Publish a Scientific Paper
École Polytechnique, Coursera

Goes beyond the thesis. Covers the full publication process. If you want your research to reach journals, not just examiners, this one matters.

6. Research for Impact
University of Cape Town, Coursera

The most underrated course on this list. Covers knowledge translation and real-world impact. Directly relevant to anyone navigating REF conversations or knowledge exchange in their institution.

7. The Art of Persuasive Writing and Public Speaking
Harvard University, Harvard Online

Academic argument and oral communication. More useful for viva preparation than most viva prep guides. Fully free, hosted directly on Harvard's platform.

8. Data Analysis: Visualisations in Excel
The Open University, OpenLearn

Practical and accessible. If you need to present findings clearly and data visualisation intimidates you, this is where to start. Fully free on OpenLearn.

Where I tell my students to begin: 
Start with course 1 to get your methodology foundations right. 
Move to course 3 early, before you start writing chapters. 
Add course 4 when you begin your literature review. 
Everything else depends on your specific research design.

Links to all 8 are in the pinned comment below.

Which of these have you already taken? And what would you add to the list?

Save this. Share it with your research group.

Repost to help a PhD student who needs it right now. #PhDResearch #AcademicWriting

45 PhD theses later, I can spot a weak lit review in 30 seconds. 
Every strong one follows this framework

After examining 45+ PhD theses, I noticed the same lit review structure in every strong thesis. 

And a completely different structure in every weak one.

I once had to tell a PhD student their literature review needed a complete rewrite. Six months of work. Gone.

The problem was not effort. It was structure.

Their review was a 40-page list of study summaries. "Fitzpatrick (2019) found this. Li et al (2025) found that." 

Forty pages of it. 
No themes. 
No argument. 
No gap.

I've seen this mistake in roughly 70% of the theses I examine. 
Students describe what others found. 
But they never explain why it matters or what is still missing.

So I created what I call the Examiner's Lit Review Framework. 
It works like a funnel.

Start wide. End focused.

Level 1: Broad context. What is the field and why does it matter right now?

Level 2: Specific area. What do we already know from the research?

Level 3: Critical gaps. What is missing, contested, or under-explored?

Level 4: Your contribution. How does your study fill that gap?

Every strong thesis I've examined follows this logic. 
The funnel forces you to move from description to argument. 
From "what exists" to "what's missing" to "why my study matters."

The rules are simple:

Synthesise by theme, not by author.
Group ideas, not studies.
Show how the field has evolved.
Always end with a clear gap statement.

And whatever you do, stop listing studies one by one. 
That's a bibliography, not a literature review.

That student I mentioned? She rewrote her review in 3 weeks using this framework. Her examiner called it exemplary.

Same researcher. Same topic. Same literature. Completely different result.

The structure made the difference.

What's the biggest challenge with your lit review right now? 
Drop it below.

Save this framework.
Repost for a colleague struggling with theirs.

#LiteratureReview #PhDWriting

I've expanded this into a full downloadable guide with annotated examples for each level of the funnel. See my comment below on how to access it.

Most PhD students are lied to about academic pay. 
The numbers are brutal, and almost nobody gets the real story.

I'm a Professor. I've seen the salary spreadsheets. 

The gap between what academia promises and what it pays would shock most PhD students.

Nobody told me the truth about academic pay when I started out.

They said the work was "rewarding." 
They said it was a "calling."

What they didn't say: a starting lecturer earns less than a mid-level project manager with half the qualifications.

A colleague left for industry last year. 
Same expertise. She doubled her income in month one.

Here's what bothers me most.

Universities use your passion as a negotiation tool. 
"If you really loved research, you wouldn't ask about money." 
I've heard versions of that in every institution I've worked at.

I'm not saying leave. I chose to stay. 
Intellectual freedom, shaping a field, mentoring the next generation. 
Those things matter.

But go in with your eyes open. 
Know the numbers before you commit.

Your passion is not a discount code.

Did anyone ever make you feel guilty for caring about pay in academia?

Save to compare later with industry jobs.

Repost to normalise this conversation.

#AcademicLife #HigherEducation

----
Disclaimer: These salary ranges are indicative estimates based on publicly available data from sources. Figures represent gross annual salaries for assistant professor or equivalent entry-level permanent academic roles. Actual salaries vary significantly by university type (public vs private, research-intensive vs teaching-focused), discipline, experience, contract type, and region within each country. 

These figures are not directly comparable across countries due to differences in taxation, cost of living, pension contributions, benefits packages, and purchasing power. UAE salaries, for example, are tax-free, which significantly changes the take-home picture. Exchange rates based on XE/Exchange-Rates.org (Feb 2026). 

This is meant as a starting point for conversation, not a definitive guide. If you have salary data from countries not listed here or corrections to share, please add them in the comments. The more transparent we are, the better.

Sources: ERI SalaryExpert (2025), Glassdoor (2025–2026), Academic Positions (2024), UGC 7th Pay Commission, India, University of Cape Town Pay Scales (2026)

Poor writing kills more academic careers than bad research. Here's a guide that helped an international PhD student in our programme fix it:

An international PhD student in our programme had her paper rejected three times last year.

Each reviewer said the same thing: "The science is strong but the writing lacks clarity and structure."

Her research was publishable. Her writing wasn't.

She didn't need more data. She needed a system.

That's the problem most international researchers face.

Nobody teaches you how to write academically in English. You're just expected to figure it out.

Then I found a resource that changed how I support PhD students in our programme.

Education and Training Boards Ireland published a 94-page academic writing handbook.

Completely free. Completely systematic.

It breaks the entire writing process into 5 clear steps:

Getting started with planning and brainstorming. Drafting with proper argument structure. Reviewing for clarity and logic. Editing for grammar and precision. Presenting to professional standards.

What makes it different from generic writing advice?

It gives you self-assessment tools to spot weaknesses before reviewers do.

She used it to restructure her rejected paper.

Fourth submission. Accepted with minor revisions.

Same research. Same data. Better writing system.

Good academic writing isn't talent. It's a learnable process.

Especially when English isn't your first language.

Link to free download in comments.

---

Which writing step challenges you most: drafting, editing, or structuring arguments?

#AcademicWriting #PhDLife

I help working professionals complete their PhD/DBA on time | Professor | 45+ PhD Theses Examined | Follow for posts on Research Skills, AI in Research & Academic Career Success

She failed her viva. Her research was original.
The examiner asked: 'Why this research philosophy and not another?' She had no answer.

Not because she was a bad researcher.

Because nobody had ever explained how the pieces connect.

Three years of work. One question. No answer.

Your research methodology is not a menu.

It's a universe. And every layer connects to every other.

Your philosophy determines your approach. Your approach determines your methodology. Your methodology determines your methods.

Get the first layer wrong and nothing that follows makes sense.

Layer 1: Research Philosophy

Your worldview. What reality is and how we can know it.

Positivism. Reality is objective and measurable. Home of quantitative research, surveys, and hypothesis-testing.

Interpretivism. Reality is constructed. You cannot separate the researcher from the research. Home of interviews, ethnography, thematic analysis.

Pragmatism. Whatever best answers your research question. Mixed methods live here.

Critical Realism. Reality exists but we can only access it imperfectly. Common in health and complex social research.

Layer 2: Research Approach

Deductive: start with theory, test against data. Fits positivism.
Inductive: start with data, build theory. Fits interpretivism.
Abductive: move between both until something explains it. Fits pragmatism.

These follow from Layer 1.

Layer 3: Methodology

The logic behind your research design. Not the tools.

Grounded theory lives in interpretivism. Experimental design lives in positivism. Mixing them without justification is where theses fall apart.

Layer 4: Methods

Interviews. Questionnaires. Observation. Experiments.

Methods are the layer most students focus on first.

That's the wrong order.

The mistakes I see in 80% of theses.

Mistake 1. Choosing philosophy based on supervisor preference, not the research question. "My supervisor uses surveys so I use surveys." That's a method. Not a reason.

Mistake 2. Mixing incompatible philosophy and methods. An interpretivist question answered with a 400-person survey. The numbers feel safe but don't answer what the study asked.

Mistake 3. No philosophical justification in the methodology chapter. "It's common in this field" is not a justification. It is a habit dressed as reasoning.

The question every examiner asks is this:

Why these choices and not others?

Ground your answer in your research question. You will pass.

I've turned the full framework into a downloadable PDF, with a decision tree and worked examples for each philosophy. Link in the pinned comment.

Which research philosophy does your study follow? Drop your topic below and I'll suggest the best fit.

Save this. You'll need it when your methodology chapter gets difficult.

Repost to help a PhD student who's stuck on their research design. #PhDResearch #ResearchMethods

I turn 45 today. I moved to the UK at 17 with one suitcase and no English-speaking friends. Here are 10 things I wish someone had told me at each stage of the journey.

At 17, I left Greece to study in a country where I barely understood the lectures. 
I sat in the back row and wrote down words I didn't know. 
The list was longer than my notes. 

Lesson one: discomfort is not a sign you're in the wrong place. It's a sign you're growing.

At 20, I graduated in Linguistics. At 21, I finished a Masters with distinction and registered for a PhD in Computer Engineering. 
People thought the jump was reckless. 

Lesson two: your path doesn't need to make sense to anyone but you.

At 22, I won a prestigious PhD scholarship. 
It felt like validation. But the imposter syndrome didn't leave. 

Lesson three: external recognition never fixes internal doubt.

At 23, I started working as a research assistant while completing my PhD. 
I was exhausted. I nearly quit twice. 

Lesson four: the people who finish are not the smartest. They're the ones who kept showing up.

At 25, I graduated my PhD and became an Assistant Professor. 
I thought I'd arrived. I hadn't even started. 

Lesson five: every finish line is someone else's starting block.

At 32, I became an Associate Professor. 
It took 7 years of rejected papers and failed grants. 

Lesson six: the years between promotions teach you more than the promotions themselves.

At 39, I started leading an international research institute. 

Lesson seven: academia prepares you to think. It doesn't prepare you to lead. Learn that separately.

At 40, I became a full Professor. 
Lesson eight: fulfilment comes from the work, not the title.

At 42, I started sharing on LinkedIn with 1,650 followers. 
Most colleagues thought it was a waste of time. 

Lesson nine: the best time to start building something new is when nobody is watching.

Today at 45: 20+ PhD students graduated, 40+ mentored and supervised, £10 million in research funding, 135+ publications, 228,000 people in this community.

Lesson ten: no career is a straight line. 
Mine started with a suitcase and a language I could barely speak. 
If you're at the beginning and it feels impossible, you're exactly where I was.

Keep going.

What's the one lesson your career has taught you so far? 

I'd love to hear it.

Repost if this is relatable.

#AcademicLife #PhDJourney

Reviewers tore apart my thematic analysis during my PhD. "No methodological rigour." Three words that crushed me. Here is how that feedback saved my career.

I had spent 6 months doing interviews. Hours of transcribing. Weeks of reading and re-reading transcripts.

And it was all unusable.

My mistake? I thought qualitative research was just reading transcripts and highlighting interesting quotes.

I had no system. No framework. No structure.

I was coding randomly. Picking themes that "felt right." Ignoring data that didn't fit my assumptions.

The reviewers saw right through it.

That rejection forced me to start over. And starting over led me to Braun and Clarke's thematic analysis framework.

It changed everything.

Here are the 6 phases that saved my qualitative research:

Phase 1: Familiarisation
Read your data. Then read it again. And again. Take notes. Let the data speak before you start labelling anything.

Phase 2: Generating initial codes
Go through every transcript line by line. Code small. Code everything interesting. Don't try to find themes yet.

Phase 3: Searching for themes
Group your codes into potential themes. Look for patterns. Some codes will cluster naturally. Others won't fit anywhere. That's okay.

Phase 4: Reviewing themes
Check if your themes actually work. Do they hold up against the data? Some themes will merge. Others will fall apart. Be honest with yourself here.

Phase 5: Defining and naming themes
Each theme needs a clear story. What does it capture? Why does it matter? If you can't explain it in one sentence, it's not refined enough.

Phase 6: Writing up
Weave your themes into a clear narrative. Use data extracts as evidence. Connect your findings back to your research questions.

One thing I wish I knew earlier.

There are two main approaches. 
Reflexive thematic analysis lets themes emerge from the data. 
Codebook thematic analysis uses a pre-defined framework to guide coding.

Most researchers mix them up. Or worse, they don't declare which one they're using.

Reviewers notice.

The biggest mistakes I see in thematic analysis:

Treating it as a simple summary of what people said. 
Jumping to themes before properly coding. 
Not being transparent about your own biases. 
Using thematic analysis because it seems "easy" without understanding its depth.

After I rebuilt my analysis using Braun and Clarke's 6 phases, the same reviewers who rejected me praised the rigour of my work.

The framework didn't just fix my paper. It transformed how I approach all qualitative research.

Are you using thematic analysis in your research? What phase do you find most challenging?

#ThematicAnalysis #QualitativeResearch

I lost a great PhD student because I focused on the research and not enough on the person.

Most PhD supervisors were never trained to supervise. 

No certification. 
No accountability.
No pastoral training.

We're appointed for our research. 
Not our ability to support a human being through one of the hardest experiences of their life.

I was one of them.

I now ask "how are YOU doing?" before "how is the research going?"

One question. Different supervision entirely.

What's the one thing a great supervisor does that most supervisors don't?

Repost if this rings true. 

#PhDSupervision #AcademicLife

Journals rejected my null findings 7 times. Then I published them myself. And they got more attention than my positive results ever did. Here's what happened:

I ran a study that showed no significant effect.

The result was important. It challenged a common assumption in my field.

But journals didn't want it.

"Not novel enough."
"Lacks sufficient impact."
"Consider a different outlet."

Seven rejections over two years.

Meanwhile I watched flashy positive results get fast tracked. 

Even when the methods were weaker than mine.

This is the publication bias problem.

Important findings get buried because they don't fit what journals want. 

Entire research directions get skewed because null results never see daylight.

I had already been using AllSci Corp. to map literature and explore related hypotheses.

Then I realized something.

If journals weren’t going to publish my null findings, I could share them directly on the same platform I was already using for discovery.

AllSci is a free platform that lets you publish your ideas without waiting for journal approval.

Here's how it works in 4 steps:

Step 1: Create your hypothesis
→ Write your hypothesis at any stage of investigation
→ No formatting requirements or submission fees

Step 2: Connect to the literature
→ AI automatically links your idea to 12 million related hypotheses
→ See supporting and refuting evidence from existing research

Step 3: Get discovered
→ Your work becomes visible to the global research community
→ Other researchers find your ideas through semantic search

Step 4: Earn credit
→ Establish authorship for early conceptual contributions
→ Build recognition even before formal publication

My null findings paper sparked conversations I never expected.

Other researchers reached out saying they had similar results. 

We connected. We collaborated. We built something bigger.

All because one platform decided truth matters more than hype.

Publication bias won't fix itself. 

But we can stop feeding it.

What null findings have you buried because journals wouldn't publish them?

Try AllSci free: www.allsci.com

#OpenScience #AcademicPublishing

AllSci Corp.

The night she almost quit her PhD, one question changed everything.
She sent me a message at 11pm. "I'm done. I can't do this anymore."

I almost missed the message. This PhD Planner changed everything.

She was 18 months into her PhD. Working full-time. Raising a family. Her supervisor had gone silent for 6 weeks. Her data analysis was a mess. She had no plan. No structure. No hope.
 
When we spoke the next morning, she told me she had already drafted the withdrawal email. She was going to send it that Friday.
 
I didn't tell her to push through.
I didn't give her a motivational speech.
I asked her one question: 

"What does your next 6 months look like, step by step?"
 
She couldn't answer. Nobody had ever asked her that.
 
So we built it together. We broke her entire PhD into fortnightly checkpoints. Each one had a single, clear deliverable. 
 
The first two weeks were rough. 
She almost cancelled twice. 
But she hit her checkpoint. 
Then the next one. 
Then the next.
 
Within 6 weeks, she had a working structure for the first time.
 
She stopped feeling lost and started feeling in control.

The method was simple:
Stage 1: Map every remaining milestone with a target date.
Stage 2: Break each one into a single deliverable every two weeks.

Not "work on chapter 3."
Instead: "Submit 2,000 words of my methodology rationale by March 14."
 
She submitted her thesis 3 months ahead of schedule. 
She passed with minor corrections. She now has 3 publications.
 
Today she's Dr Helen.
 
Here's what I've learned from experiences like hers:
 
Most PhD dropouts are not caused by lack of ability.
They're caused by lack of structure.
 
1 in 3 PhD students consider quitting in year one. 
Not because the research is too hard. 
Because nobody shows them how to plan the journey.
 
If you're in that dark place right now, you're not broken. 
You're unsupported.
 
And that can be fixed.
 
If you're in that dark place where quitting feels logical, what would make you stay?

Save for when you need it.

Repost to help a PhD student who might need this today.

#PhDLife #AcademicMentoring

--- 
I've put together the exact milestone planning template I used with this student: blank templates, worked examples for PhD and DBA, and a fortnightly checkpoint log. Free download → link in my newsletter.

How to build a complete conference or defense presentation slide deck in 12 minutes. A student showed me.

After supervising 25+ PhDs to completion, I thought I knew every workflow hack. 

Last week, a student 3 weeks from defense proved me wrong.

Zero slides. Full panic. 12 minutes later? A complete defense deck structure.

I felt stupid for not knowing this sooner.

For years, I lost 3 hours every time I turned research notes into slides.

My thinking was clear. My ideas were ready. But the reformatting? Painful.

Copy from notes. Paste into PowerPoint. Rebuild the structure. Realize my thinking evolved. Start again.

I thought this was just part of academic life.

I was wrong.

Here's what she showed me:

She opened Claude. Connected Gamma (took 2 minutes). Pasted her chapter summaries. Asked Claude to help structure the narrative arc.

Then, without leaving Claude, she generated the presentation layout.

12 minutes later: 15-slide defense deck. Ready to refine.

Not AI-written content. Her research. Her findings. Her voice.

Just... structured. Designed. Ready to polish.

My exact words: "Why did nobody tell me about this earlier?"

The old workflow:

→ Think in one tool
→ Copy to another
→ Rebuild the structure
→ Realize thinking evolved
→ Start again

Hours lost. Momentum killed.

The new workflow:

→ Plan in Claude
→ Generate structure & design in Gamma (inside Claude)
→ Refine both together
→ Thinking evolves. Deck evolves with it.

This isn't about AI writing your content.

It's about AI handling the mechanical design work so you can focus on your research.

I've put together 50+ prompts specifically for academic presentations.

Conference talks. Poster layouts. Defense structures. Teaching materials.

What's your most time-consuming presentation task right now?

I've put together 50+ prompts specifically for academic presentations.
Conference talks. Poster layouts. Defense structures. Teaching materials.

Repost ♻️ and comment 'FLOW' to get the complete guide within 72 hours.

Save this for the next time you need to build a deck from scratch 🔖

Gamma

#PhDLife #AcademicTwitter

Gamma

After mentoring working professionals through PhDs and DBAs for over a decade, I can tell you: most choose the wrong one.
Universities won't tell you this. Both programmes pay the same fees annually.

Here's what they should tell you before you sign up.

An MBA teaches you to apply existing frameworks. 
- You learn strategy, finance, marketing, operations. 
- You graduate with a toolkit built by other people. 
- It's broad. It's fast.

It's designed to move you up inside existing structures.

A DBA teaches you to create new knowledge. 
- You identify a real problem in your industry. 
- You design original research to solve it. 
- You graduate having contributed something that didn't exist before. 
- It's deep. It's slow. It's designed to make you a thought leader.

Completely different outcomes. Same price tag.

Choose an MBA if you want to climb the corporate ladder faster, build a wide professional network, and apply proven models to your organisation.

Choose a DBA if you want to solve a specific industry problem with rigorous research, contribute original knowledge to your field, or transition into academia or senior consulting.

The time commitment is different too. 

An MBA is typically 1 to 2 years. 
A DBA is 3 to 5 years of intensive research alongside your career. 

Nobody warns you about that gap.

And here's what really bothers me. 
University admissions teams rarely explain this difference clearly. 
Why would they? 

Both programmes generate the same revenue annually. 
Whether you finish or not.

I've watched executives invest 3 years into a DBA when an MBA would have got them promoted in 12 months. 

I've watched others complete an MBA and realise they wanted to create knowledge, not just consume it.

The wrong choice doesn't just cost money. 
It costs years you cannot get back.

MBA or DBA: which path are you considering and why? 

I'll give you an honest answer below.

Save it and repost to help a colleague deciding.

#DBA #PhD

I thought I knew every major finding in my field. Then an AI found research I'd missed after 15 years in minutes. Here's what happened.

I've been researching design for health and wellbeing for over two decades. I've published 130+ papers in the field. 
I thought I knew the landscape.

Well, that was until a colleague of mine recomend to check out Patsnap Eureka R&D, a free AI search engine built for R&D that covers 200 million patents and 210 million scientific papers.

I started by asking it to map how my field has evolved over 20 years. 
It identified sub-fields and saturation points I hadn't spotted. 

The results pulled from both patents and academic papers in a single search. Things I would have needed weeks to piece together manually.

Then I described a real engineering challenge I'm working on. 
It generated solution directions from consumer electronics and automotive patents. 
Work from industries I'd never have thought to search using Google Scholar.

Finally, I checked my current research for novelty. 
It surfaced patents that don't appear in Scopus or any academic database I use.

The biggest surprise was the cross-field discovery. 
Patents are organised by technical function, not academic discipline. 
An engineer at a company like Bosch might have solved your problem in 2019 using completely different terminology. 
Traditional searches miss it because the words don't match. Semantic search catches it because the meaning does.

What stood out to me: every result links back to its original source. 
Three separate modes match different stages of research. And it's free to use.

What's worth knowing: it's strongest for STEM and applied sciences. 
The three modes take some learning. 
And like any AI tool, results still need your own critical judgement.

If I missed relevant work after 15 years in my own field, there's a good chance you're missing it too.

Try it yourself. Link in comments.

Patsnap Eureka

Have you ever searched the patent literature for your research topic? 
What did you find? 

🔖 Save this for your next literature review. 
♻️ Repost to help a researcher who needs to hear this.

taplio

The Battle

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The Battle

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