Akhil Reddy Peeketi
80fb7d6d5a
- README.md: rewritten as landing page (~160 lines) — problem/solution hook, "what makes this different", example output links, "try it first" fast path, streamlined quickstart - DOCS.md: new reference doc with architecture, concepts, customization tables, design decisions, and FAQ (moved from README) - Example output: compiled resume + cover letter PDFs for Dr. Jordan Chen (fictional) applying to Whitfield University faculty position - Example .tex sources in resume_builder/examples/output/ - GitHub repo description and topics updated
44 lines
3.4 KiB
TeX
44 lines
3.4 KiB
TeX
% Example output — Dr. Jordan Chen cover letter for Whitfield University
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% Generated by claude-resume-kit for demonstration purposes
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% This is a fictional researcher; all data is fabricated.
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\documentclass[11pt,a4paper,roman]{moderncv}
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\usepackage[english]{babel}
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\moderncvstyle{classic}
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\moderncvcolor{green}
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\usepackage[utf8]{inputenc}
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\usepackage{ragged2e}
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\usepackage[scale=0.79]{geometry}
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\usepackage[version=4,arrows=pgf-filled]{mhchem}
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\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par}
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\name{Jordan}{Chen}
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\address{Richland, WA 99354}
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\phone[mobile]{+1 (555) 123-4567}
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\email{jordan.chen@email.com}
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\begin{document}
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\recipient{To}{Hiring Committee\\Department of Biomedical Engineering\\Whitfield University\\Westbrook, MA 02481}
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\date{\today}
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\opening{Dear Members of the Hiring Committee,}
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\makelettertitle
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\begin{justify}
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Your department's work at the intersection of structural biology and therapeutic design resonates with the research program I have built over the past three years: using machine learning to accelerate protein engineering decisions that would otherwise take months of experimental iteration. As a postdoctoral researcher developing ML-guided enzyme screening pipelines, I am excited to apply for the Assistant Professor position in Computational Protein Engineering (BME-2026-0042), where I would establish an independent group bridging data-driven protein design and biomolecular simulation.
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At Lakewood University, I fine-tuned the ESM-2 protein language model on 45,000 experimental melting temperatures to screen 8,500 enzyme variants for industrial thermostability, compressing what would have been over a year of wet-lab work into 48 hours of computation. Five of our top seven candidates were confirmed experimentally by collaborators via differential scanning calorimetry. I then co-developed an open-source transfer learning framework that reduces labeled training data requirements by 60\%, now adopted by four external research groups. More recently, I extended our ML pipeline to predict enzyme tolerance across eight organic co-solvent systems, opening a pathway toward engineering biocatalysts for green chemistry. This research trajectory, from classical MD to ML-accelerated protein engineering, reflects the kind of program I would build at Whitfield.
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My doctoral work at Westfield established the simulation foundations that make this ML approach rigorous rather than purely correlative. I developed enhanced sampling protocols that predict protein folding temperatures within 8 K of experiment, benchmarked four force fields for intrinsically disordered proteins, and calculated ligand binding free energies across three drug target families with sub-kcal/mol accuracy. I also built the curated thermostability database that directly enabled my postdoctoral ML work. Throughout, I mentored three graduate students and developed computational lab modules now used department-wide.
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I would welcome the opportunity to contribute to Whitfield's strengths in biomaterials and therapeutic design. I could teach courses in computational biology, molecular modeling, and machine learning for biomedical applications. I look forward to discussing how my research program would complement your department's existing strengths and the collaborative opportunities available through your HPC infrastructure.
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\end{justify}
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\vspace{0.3cm}
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{Sincerely,\\
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Jordan Chen, Ph.D.\\
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Postdoctoral Research Associate\\
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Lakewood University}
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\end{document}
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