The Technical Language Problem Nobody Talks About

Here's a tension most SOP guides don't address: faculty want to see technical language, but they also complain about "jargon soup."

So which is it?

After synthesizing advice from faculty at MIT, Berkeley, Georgetown, Purdue, and other top programs, the answer is clear: technical language should function as evidence, not decoration. When jargon connects to specific work you've done, it strengthens your application. When it floats disconnected from context, it signals superficial understanding.

The distinction isn't between technical and accessible writing. It's between language that demonstrates thinking and language that performs expertise.

What Faculty Actually Mean by "Use Technical Language"

Penn Career Services tells PhD applicants: "The admission committee will be comprised of faculty members in the department you are applying to work in as a graduate student. Don't be afraid to use professional or technical language."

But MIT's Biological Engineering CommKit adds the crucial caveat: "Use technical language where appropriate; however, avoid jargon and acronyms specific to your sub-field. Faculty from research areas across the department may read your application."

These aren't contradictory. Your readers are experts in adjacent areas, not your specific niche. They can handle technical language. What they can't handle is inside-baseball vocabulary that assumes everyone shares your exact background.

Mark Fuge at the University of Maryland captures the calibration problem well. He warns that saying "I'm interested in Mechanical Engineering" signals insufficient focus. But hyper-specific claims like "I want to work on agent-based architectures for swarm-based, unmanned underwater vehicles" risk excluding yourself from faculty whose projects don't match precisely.

The sweet spot: specific enough to show depth, accessible enough to show you can communicate.

The PEA Framework: Testing Every Technical Term

Here's a practical test you can apply to every technical term in your SOP. A term belongs only if it does at least one of these jobs:

P - Purpose

Does this term clarify your research question or motivation? Does it help the reader understand the why and what of your work?

Passes the test: "I developed a transformer-based classifier to detect early signs of diabetic retinopathy in fundus images."

Fails the test: "I'm interested in transformers, attention mechanisms, computer vision, and healthcare AI."

The first uses "transformer-based classifier" to explain what you actually built. The second lists buzzwords without anchoring them to any specific work.

E - Evidence

Does this term attach to concrete work you did - methods used, data generated, results achieved?

Passes the test: "I implemented a Monte Carlo tree search algorithm that reduced decision latency by 34%."

Fails the test: "I have experience with Monte Carlo methods."

The difference is action. The first shows you using the method and measuring results. The second just claims familiarity.

A - Alignment

Does this term demonstrate fit with the program - connecting your work to faculty research, lab resources, or departmental strengths?

Passes the test: "Professor Chen's 2024 paper on topological data analysis raised questions about stability in high-dimensional spaces that my work on persistent homology directly addresses."

Fails the test: "I'm interested in Professor Chen's work on machine learning."

The first shows you've read specific work and thought about how yours connects. The second could be written by anyone who skimmed a faculty webpage.

The Anchor Principle: Every Term Needs a Verb

A simple mechanical fix for floating jargon: put every technical term next to:

• An action verb: built, trained, derived, implemented, validated, compared
• An output: model, dataset, protocol, analysis, paper
• A result: improvement, error reduction, speedup, insight

Floating (weak): "I used BERT, attention, self-supervised learning..."

Anchored (strong): "I fine-tuned BERT on a dataset of 50,000 clinical notes, comparing attention patterns across diagnostic categories. This analysis revealed systematic differences in how the model weighted symptom descriptions versus demographic information."

The first is a list. The second is evidence of work done and thinking applied.

Earned Jargon: Introduce Before You Deploy

Technical writing scholarship calls this "earned jargon." You introduce terms strategically rather than assuming the reader knows them.

Nature Portfolio's author guidance captures it: "When essential, specialized terms should be defined at first use."

A successful astronomy PhD applicant (TakeruK on GradCafe) demonstrates this approach: "I might have written a sentence like 'We used the numerical integrator SWIFT to compute....' This way, someone who works on my topic will know exactly what I'm talking about since SWIFT is one of the standard packages, but someone else in physics/astronomy can still get the idea that I did a numerical computation."

The pattern is simple. First mention: provide context. Later mentions: use the shorthand.

Two quick techniques:

Appositive definition: "X, a method for [brief explanation], allowed me to..."

Short clause: "X, which estimates [what it does] by [how], revealed that..."

These take 5-10 extra words but dramatically improve accessibility without sacrificing precision.

Show, Don't Tell - The STEM Version

You've heard "show, don't tell" applied to creative writing. Here's what it means for technical SOPs.

Telling (weak): "I am passionate about machine learning."

Showing (strong): "I spent six months debugging a custom neural architecture for protein structure prediction, eventually identifying a gradient instability that had been masked by our normalization layers."

Telling (weak): "I have strong analytical skills."

Showing (strong): "When our initial results contradicted published findings, I systematically varied each experimental parameter until isolating a contamination source in our reagent preparation."

Notice how the "showing" versions contain technical language, but that language does work. It proves competence by attaching to specific actions and outcomes.

Natalie Grinblatt, former Michigan Ross admissions dean, puts it this way: "Adcoms read thousands of applications. We don't just want to know that you're passionate or determined, we want to see you exhibiting these qualities."

Her advice for writers: "Think 'evidence,' not 'adjectives.'"

The Six Jargon Mistakes Faculty Actually See

Based on faculty feedback and writing lab guidance, these are the technical language failures that hurt applications:

1. Buzzword Soup

"I am passionate about deep learning, machine learning, NLP, computer vision, and AI systems."

This tells readers nothing. Every term needs an anchor. If you mention a method, explain what engaging with it taught you. If you name a tool, connect it to a problem you solved.

2. Assuming Excessive Expertise

Using highly specialized acronyms without explanation. Physics forum feedback captures this: "You should really define this acronym the first time you use it because your audience is the general Physics department faculty, not just the people that study your field."

3. Technical Detail Overload

WriteIvy's analysis of weak SOPs: "In paragraph three, we usually encounter a research project. And boy do we learn everything about this project. The author leaves no stone unturned. They tell us every menial detail."

Focus on motivation, your contribution, and outcomes. Not exhaustive methodology.

4. Jargon Masking Shallow Understanding

Georgia Tech faculty warn that "statements that praise our department on its excellence in a topic where no current research is going on raise a red flag to the committee, and these applicants are generally rejected."

Using field vocabulary doesn't prove understanding. Correctly connecting concepts does.

5. Technical Work Without Significance

Multiple sources emphasize explaining work "in plain English" because this demonstrates deep understanding. If you can only describe your research using jargon, you may not understand it well enough for graduate-level work.

This is also critical practice for grant writing, where you'll need to explain specialized work to broad review panels.

6. Outdated Research Focus

Chris Blattman at UChicago notes that "students are focused on the research frontier 10 years ago (because those are the papers they read in their classes) and are not clued in to some of the current puzzles and priorities."

Your technical vocabulary should reflect current work in the field, not just classic papers from your coursework.

Quantification Creates Credibility

MIT's EECS Communication Lab emphasizes: "We are all scientists and engineers; our line of work is inherently quantitative. Quantification is a quick and easy way to add context, lend credibility to your experiences, and impress the reader."

Weak: "I spent some time working on a project about optimization."

Strong: "I spent two semesters developing a convex optimization framework that reduced training time by 40% on benchmark datasets."

Numbers ground abstract claims. Team size, dataset size, accuracy improvements, time invested, publications produced. These details make technical descriptions concrete.

A 5-Question Jargon Audit

Before submitting, run every technical term through these questions:

1. Necessity: Is this the simplest accurate term? If a clearer word exists, use it.

2. Audience: Would a faculty member outside your subfield understand it? If not, add a brief definition.

3. Evidence: Is it tied to something you actually did? If not, either anchor it to specific work or remove it.

4. Outcome: Does it help the reader see impact or learning? If not, add consequence or cut it.

5. Alignment: Does it connect to why this specific program? If it's generic, it's probably decoration.

The Two-Reader Test

Have two people read your SOP:

• One person in your field
• One person in an adjacent field (same discipline, different specialty)

Ask them both to underline anything that felt like "inside baseball." If both flag the same term, revise it. If only the adjacent-field reader flags it, add a brief definition.

This directly targets what psychologists call the "curse of knowledge" - the tendency to assume others know what you know.

The 20-Second Skim Test

Faculty read under information overload. Chris Blattman frames the applicant's job as: "send the clearest signal of your potential as a researcher and minimize noise."

After a 20-second skim of your SOP, can a reader answer:

• What specific area do you want to study?
• What's the strongest evidence you can do this work?
• Why this program specifically?

If technical language is crowding out these signals, cut it. Technical density should clarify your qualifications, not obscure them.

Field-Specific Calibrations

Computer Science

Tolerates higher technical density due to rapid field evolution, but buzzwords face particular scrutiny. Krishna Murthy Jatavallabhula at Mila warns: "Do not feign interest in a particular research area or direction you have no intention of pursuing or just because it's trendy."

CS SOPs should read like a mini research proposal - articulating why your direction matters and what success would enable.

Life Sciences

Emphasize understanding of experimental design and scientific method. UCSD's Biology PhD guidance states: "You can assume you are writing to a biologist, but your description must be understandable to a reader who is not an expert in the specific field of your research."

Quantify lab experience specifically. How many protocols did you develop? By what percentage did you improve a process?

Physics

Requires balancing precision with accessibility for the general department audience. Carnegie Mellon physicist Markus Deserno notes: "Faculty on admissions committees have to read dozens, sometimes hundreds of SOPs, and if any one of them drones on for just a tad too long, they lose interest in the writing — and maybe in you."

Engineering

Demands concrete achievements with measurable outcomes. MIT Mechanical Engineering CommKit advises transforming course achievements into demonstrated capabilities: instead of "I received an A in a graduate-level CFD course," write "A graduate-level computational fluid dynamics course challenged me to implement custom boundary conditions for turbulent flows."

The Paragraph Structure That Works

A strong SOP paragraph often follows this internal structure:

1. Purpose sentence (broad): What problem or question are you addressing?

2. Evidence block (technical, anchored): What exactly did you do? What methods did you use? What was the outcome?

3. Reflection sentence (skill/trajectory): What did you learn that prepares you for graduate work?

4. Alignment bridge: Why does this point to this program or faculty member?

This structure makes technical language inevitable rather than decorative. Terms appear because you're describing actual work.

A Final Checklist

Before submitting, verify:

• Each technical term connects to specific experience or a specific research question
• A faculty member outside your immediate subfield could follow your description
• Specialized terms are introduced before being deployed
• You're showing expertise through evidence rather than telling readers you're expert
• Your technical language answers "so what?" - connecting to fit, growth, or direction

The Bottom Line

Faculty don't want you to avoid technical language. They want you to use it as evidence.

The distinction successful applicants make: technical terms are tools for demonstrating competence, not costumes for appearing competent.

When jargon emerges naturally from describing real work and real thinking, it strengthens applications. When it floats free - listing buzzwords, assuming excessive reader knowledge, or decorating rather than communicating - it reveals exactly what applicants hope to hide.

The question isn't whether to use technical language. It's whether each term is doing work.

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Sources

• MIT Communication Lab - Statement of Purpose Guide
• Mark Fuge (UMD) - IDEAL Lab SOP Tips
• Chris Blattman (UChicago) - PhD Statement of Purpose
• Nathan Schneider (Georgetown) - Application Advice
• Nature Portfolio - Author Guidelines
• Association for Psychological Science - How Abbreviations Hamper Communication

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Want feedback on whether your technical language is working as evidence? Try GradPilot's SOP review - we analyze how your statement reads to faculty reviewers.