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7 STEM Fellow - Scientific AI Evaluation Resume Templates with Example

By Ria · Last Updated On October 16, 2026
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This guide shows you how to build a clear, focused resume for a STEM Fellow - Scientific AI Evaluation role. You will learn how to present relevant research evidence and choose a layout that makes your qualifications easy to review.

The examples show one resume in 7 layouts. Compare the image previews, then open a layout in the editor to explore how its sections are arranged.

For this role, connect scientific judgment with careful evaluation of AI-generated research claims. Highlight relevant work with Claude and Claude Code, experimental design, data interpretation, Python workflows, and research planning when these match your experience.

What Makes a Great STEM Fellow - Scientific AI Evaluation Resume in 2026

A strong resume makes your research focus, scientific judgment, and evaluation skills easy to find. Use clear headings and concise bullets to show how you approach complex questions and assess evidence.

Use relevant terms such as Claude, Claude Code, scientific evaluation, experimental design, dataset development, model assessment, and Python workflows. Support each skill with accurate examples from your research, tools, or education.

Note: Resume examples are for educational purposes only. Use only information that accurately reflects your experience and qualifications. References to real organizations do not imply affiliation or endorsement.

#1 Simple One Column Format Resume Example

A one-column body keeps the resume's sections in one reading path, making the order easy to follow.

 
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Why This Format Works

  • One reading path lets reviewers follow the summary, skills, and education in order.
  • Clear section breaks help locate research strengths without scanning parallel columns.
  • An ATS-readable text structure keeps core qualifications in a familiar sequence.

Best For

  • Single-path section review
  • ATS-readable text structure
  • Text-first resumes
  • Sequential career summaries
  • Clear skills and education sections

Resume Keywords

  • Scientific research
  • Claude
  • Claude Code
  • scientific evaluation
  • Simulation development
  • analysis planning
  • interpretable results
  • Mechanistic reasoning
  • analysis scripts and evaluation workflows
 

#2 Simple Two Column Format Resume Example

Two columns place resume content in parallel areas, giving reviewers another way to scan the sections.

 
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Why This Format Works

  • Parallel columns let reviewers find skills and education without passing through every section.
  • Separate content areas can keep research strengths distinct from supporting qualifications.
  • An ordered resume flow helps reviewers connect the summary with later section details.

Best For

  • Parallel section scanning
  • Distinct category grouping
  • Skills and education visibility
  • Compact content organization
  • Two-column reading preference

#3 Plain One Column with Shaded Labels Resume Example

A one-column body presents the resume in a direct sequence, which suits readers who prefer a linear review.

 
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Why This Format Works

  • One continuous column supports a steady review of the summary, skills, and education.
  • Section labels give reviewers clear places to find resume categories.
  • A linear structure keeps research qualifications in a familiar order for ATS review.

Best For

  • Resume with sequential sections
  • ATS-readable content order
  • Text-focused review
  • Easy section location
  • Full-width body text
 

#4 One Column Format with Photo Right Resume Example

This one-column resume includes a photo, while its text sections follow a single reading path.

 
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Why This Format Works

  • The one-column reading order keeps the resume's text sections easy to follow.
  • A photo adds a visual element while the resume content remains text-led.
  • Section headings help reviewers locate education and technical skills.

Best For

  • One-column text review
  • Resume with a portrait
  • Linear section navigation
  • Photo-inclusive document preference
  • Skills and education in sequence
 

#5 Clean Two Column Sidebar Format Resume Example

The two-column body organizes resume content in parallel areas for readers who prefer a structured scan.

 
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Why This Format Works

  • Two columns separate resume information, helping reviewers move between skills and education.
  • Distinct content areas organize research strengths without requiring one long section sequence.
  • Section grouping gives technical skills and education clear places in the resume.

Best For

  • Parallel content review
  • Distinct resume categories
  • Skills and education access
  • Structured section scanning
  • Two-column information layout
 
 

#6 Two Column Format Resume Example

Two columns give the resume a side-by-side reading structure, useful when reviewers want to scan separate areas.

 
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Why This Format Works

  • Side-by-side reading lets reviewers compare core strengths with supporting resume sections.
  • Separate columns reduce the need to move through every section in sequence.
  • Section grouping helps education and technical skills remain easy to locate.

Best For

  • Side-by-side section review
  • Separate content areas
  • Shorter scanning paths
  • Skills beside supporting details
  • Parallel resume organization
 

#7 Photo Header Two Column Format Resume Example

This two-column resume includes a photo, pairing a portrait with parallel areas for the document content.

 
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Why This Format Works

  • Two columns organize resume sections while the photo adds a visual element.
  • Parallel content areas help reviewers locate education and skills efficiently.
  • The portrait adds visual presentation without replacing the resume's structured content.

Best For

  • Two-column section scanning
  • Resume with a portrait
  • Parallel content organization
  • Photo-inclusive resume preference
  • Separate skills and education areas
 
 
 

Why Build Your STEM Fellow - Scientific AI Evaluation Resume with ResumeInMinutes

ResumeInMinutes lets you compare resume image previews and open an example in the editor. A clear one-column structure can help readers follow your research focus, skills, education, and supporting evidence.

  • ATS-friendly section structure
  • Research and skills organization
  • Role-focused keyword guidance
  • Clear education and experience sections
  • Space for tools and research methods
  • Resume customization in the editor

Choose a layout that makes your qualifications easy to scan. Keep claims accurate, and use specific details to explain your contribution to research, evaluation, or analysis work.

 

See Also

 

Frequently Asked Questions

Are these STEM Fellow - Scientific AI Evaluation resume templates ATS-friendly?

The layouts use readable sections and familiar headings that can support ATS parsing. Use clear wording for relevant skills, such as scientific evaluation, Claude, Claude Code, experimental design, and model assessment.

Are these different resumes or layouts of one example?

This page shows one STEM Fellow - Scientific AI Evaluation resume example in different layouts. Compare the image previews and open a preferred version in the editor.

What should a STEM Fellow - Scientific AI Evaluation resume include?

Include a focused summary, education, relevant research experience, technical skills, and tools. Add clear evidence of scientific judgment, evaluation work, experimental design, data interpretation, or research planning when it reflects your background.

How can I show that I use Claude and Claude Code?

Name the tools in your skills section when you have used them. In a relevant experience or project bullet, explain what you did, such as literature synthesis, analysis planning, script development, or evaluation workflow design.

How can I describe scientific AI evaluation work?

State the research question, the evaluation method, and what the results showed. Explain how you used evidence or meaningful controls to assess a model's claims, without overstating the findings.

Should I include a photo on my resume?

For U.S. applications, a no-photo resume is generally the safer choice. Use a photo only when it is customary or requested in the region where you are applying.

ATS score and resume checks

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ATS Score 74%

How we calculate this score

  • Keywords: 60% of the total. AI extracts keywords from your job description. If you do not provide one, a standard job description for the title matching your resume is used. This is not a match to a specific vacancy.
  • Text-based resume: 20%. The text resume assessed here receives 100 for this part.
  • Resume quality: 20%. We start at 100 and subtract points for issues found by our resume checks, down to a minimum of zero.
  • Keyword coverage = 100 × (confirmed matches + 0.8 × possible matches) ÷ total keywords, rounded and capped at 100. Possible matches still need checking. Saved generation audits use their saved keyword percentage.
  • Final score = 60% of keyword coverage + 20% of the text score + 20% of the quality score, rounded to a whole number. Without keywords, no overall ATS score is available.

What this score means

Our scores are estimates, not employer ratings. They may contain errors and do not guarantee accurate parsing, a ranking, an interview, or a job. Check your document against the employer’s requirements. You are responsible for relying on this score.

Terms and Conditions

Score weights: Keywords 60% · Text based resume 20% · Resume quality 20%

Keywords: 9 matched + 3 possible of 20 (57%) · Resume quality: 100%

Resume quality 100% · Weight 20% · Issues (0)

Details
  • ✓Name present: Matias Choi
  • ✓Email format: matias.choi@example.com
  • ✓Phone format: (206) 555-0148
  • ✓No placeholder fields
  • ✓No repeated achievement bullets
  • ✓No empty bullets

No issues found by the current checks.

Text Based Resume 100% · Weight 20%

Parsed text
Matias Choi
(206) 555-0148
matias.choi@example.com
matiaschoi
Seattle, WA 98109
SUMMARY
Computational biologist and research leader with 16 years of experience in doctoral and postdoctoral research, biotechnology, and faculty settings. Focus on protein function, enzyme mechanisms, and experimental design, translating difficult biological questions into reproducible studies, curated datasets, and interpretable results. Since 2024, apply Claude to literature synthesis, analysis planning, and scientific evaluation, and use Claude Code since 2025 to develop analysis scripts and evaluation workflows. Combine laboratory and computational methods with evidence based review of model claims, meaningful controls, and collaborative research to investigate difficult questions in computational biology.
EDUCATION
PhD
2010
University of Washington
Seattle, WA
Coursework: Computational biology, protein biochemistry, enzyme mechanisms, experimental design
TECHNICAL SKILLS
Programming Languages: Python, R, SQL
AI Assistants: Claude, Claude Code, ChatGPT
Scientific Computing: Jupyter, NumPy, SciPy
Data Analysis: pandas, Polars, Matplotlib
Sequence Analysis: BLAST, Biopython, Clustal Omega
Protein Modeling: PyMOL, AlphaFold, Rosetta
Simulation Software: GROMACS, AMBER, OpenMM
Laboratory Platforms: Benchling, SnapGene, Geneious
Data Formats: FASTA, FASTQ, PDB
Version Control: Git, GitHub, GitLab
SKILLS
Protein function
Enzyme mechanisms
Experimental design
Scientific research
Claude
Claude Code
Scientific evaluation
Dataset development
Simulation development
Model assessment
Data interpretation
Mechanistic reasoning
Research planning
Python workflows
Literature synthesis
LANGUAGES
English (Native)
Mandarin Chinese (Intermediate)

Matched keywords (9)

  • Scientific research
  • Claude
  • Claude Code
  • scientific evaluation
  • Simulation development
  • analysis planningOriginal keyword in JD: Experimental planning
  • interpretable resultsOriginal keyword in JD: Scientific data interpretation
  • Mechanistic reasoning
  • analysis scripts and evaluation workflowsOriginal keyword in JD: Test-time compute strategies

Missing keywords (8)

  • Scientific judgment
  • PhD in a STEM discipline
  • Research problem definition
  • Model limitation characterization
  • Scientific software integration
  • Machine learning
  • Independent research
  • Cross-functional research collaboration

Possible matches (3)

  • Evaluation dataset development80% credit: 4.00 percentage points of 5.00 possible.
  • Measures of research progress80% credit: 4.00 percentage points of 5.00 possible.
  • Model performance assessment80% credit: 4.00 percentage points of 5.00 possible.
Disclaimer: All names, images, contact information, employment histories, achievements, security clearances, military credentials, and other qualifications shown in this content are fictional and used for illustrative purposes only. Any resemblance to actual persons, living or deceased, is purely coincidental.

Resume and cover-letter examples and recommendations in this article reflect general best practices and professional experience and are provided for guidance only. Results may vary based on role, industry, region, and hiring organization.

Content Accuracy and Verification: To the fullest extent permitted by applicable law, we do not represent or warrant that resume or cover-letter guidance, examples, recommendations, or related information are accurate, complete, current, error-free, or suitable for any particular purpose. You are responsible for independently reviewing and verifying the information before relying on it.

ATS and Cover-Letter Scores: ATS scores are estimates calculated using our own formula, informed by publicly available guidance on resume formatting and keyword matching. They are not scores issued or endorsed by an ATS provider or employer. Cover-letter scores are also our own assessments. Parsing, weighting, screening criteria, and results vary between systems, employers, job descriptions, and document formats. No score guarantees successful parsing, a particular ranking, an interview, or employment. Use scores as general guidance and independently check your documents against the employer’s requirements. To the fullest extent permitted by applicable law, we accept no liability for errors in scores or parsing, or losses arising from reliance on these assessments. Nothing in this notice excludes liability or limits consumer rights that cannot lawfully be excluded or limited.

Terms and Conditions
 
 
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