Akhil Reddy Peeketi
9a7c627cc3
- Add ai_fingerprint_rules.md with banned words, structural rules, and 12-item post-gen checklist - Fix Fellowships/Honors template format: --- to period separator - Fix Publications under-review template format - Update all 4 skills to load fingerprint rules during generation - Add AI scan section to critique framework - Update resume_reference and cl_reference with em-dash limits - Reduce em-dashes in example files Co-Authored-By: Akhil Peeketi <peeketiakhilreddy@gmail.com>
119 lines
6.2 KiB
Markdown
119 lines
6.2 KiB
Markdown
# Bundle: Academia
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> Role-type positioning guide for university faculty and research professor positions.
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---
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## S1: Role Profile
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**Target employers:** R1 research universities, liberal arts colleges with research programs, international universities
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**Typical titles:** Assistant Professor, Associate Professor, Research Assistant Professor, Lecturer, Postdoctoral Fellow
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**What they value (ranked):**
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1. Independent research capability with publication record
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2. Teaching experience or potential
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3. Method development (not just method application)
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4. Cross-disciplinary breadth (computational + experimental collaboration)
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5. Mentorship and advising evidence
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6. Grant-writing experience or potential for external funding (NIH, NSF)
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7. Open-source contributions and community engagement
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**Positioning strategy:** Lead with ML pipeline development and independent protein engineering results. Emphasize broadly applicable computational skills (protein language models, MD simulations, free energy methods). Show evidence of independence (first-author papers, open-source tools) alongside collaboration (experimental validation, mentorship).
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**Differentiation angle:** Not just an MD user or an ML practitioner , a bridge between biomolecular simulation and data-driven protein design, with production-quality software skills.
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---
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## S2: Summary Guide
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**Tagline pattern:** [Method developer] + [application domain] + [scale/impact metric]
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**Building blocks (pick 3-4 for summary):**
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- ML-guided protein stability prediction (ESM-2, transfer learning)
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- High-throughput virtual screening (8,500+ enzyme variants)
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- Transfer learning for low-data protein property prediction
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- Enhanced sampling MD (metadynamics, replica exchange, FEP)
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- Enzyme solvent tolerance prediction
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- Open-source tool development (200+ GitHub stars)
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- Automated screening pipeline (Snakemake, SLURM)
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- Consistent domain: enzyme engineering, protein stability, folding thermodynamics
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**Summary do's:**
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- Open with "Computational biologist" or "Protein engineer"
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- Include one quantified throughput/scale metric
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- Name 2-3 specific methods/tools
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- Close with a research vision statement
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**Summary don'ts:**
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- Do not open with "Passionate" or "Motivated"
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- Do not list more than 3 software tools in the summary
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- Do not use buzzwords without concrete backing ("cutting-edge", "novel", "innovative")
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---
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## S3: Achievement Reframing Map
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**Priority matrix for academic roles:**
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| Priority | Achievement | Why | Reframing Notes |
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|----------|------------|-----|-----------------|
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| 1 (must) | L1: Enzyme Stability Screening | Core ML pipeline development + high-impact application | Lead bullet. Emphasize 3,000x throughput and independent development. |
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| 2 (must) | L4: Transfer Learning Framework | Open-source impact, community adoption | Highlight GitHub stars and external adoption as evidence of research maturity. |
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| 3 (must) | L3: Automated Screening Pipeline | Infrastructure contribution, team enablement | Frame as "enabling 6 researchers" -- departments value force multipliers. |
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| 4 (strong) | L2: Enzyme Solvent Tolerance | Deeper enzyme engineering expertise | Natural extension of stability work into industrial conditions. Note under-review status. |
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| 5 (strong) | L5: Unfolding Pathway Analysis | Mechanistic insight from simulations | Use if JD mentions dynamics, thermodynamics, or structural biology. |
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| 6 (if room) | L6: Mentorship | Teaching and advising fit | Include for faculty positions; optional for postdoc applications. |
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**Omit from academic resumes:** Undergraduate coursework projects, non-research achievements.
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---
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## S4: Skills Guide
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**Bold tools (tools the JD will likely name or ATS will scan):**
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- **GROMACS**, **Python**, **PyTorch**, **SLURM**
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- **Machine learning** (or **protein language models** if JD uses that phrase)
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**Include but do not bold:**
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- AlphaFold2, Rosetta, OpenMM, RDKit, BioPython, MDAnalysis
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- Snakemake, Git, Bash, PostgreSQL, Linux
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**Group strategy (for skills section):**
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- Group 1 -- Simulation & Modeling: GROMACS, OpenMM, AMBER, AutoDock Vina
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- Group 2 -- Machine Learning: Protein language models (ESM-2), graph neural networks, transfer learning, PyTorch
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- Group 3 -- Programming & HPC: Python, Bash, SLURM, Snakemake, Git
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- Group 4 -- Analysis & Visualization: BioPython, MDAnalysis, ProDy, PyMOL, matplotlib
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- Group 5 -- Domain Knowledge: protein engineering, drug discovery, free energy methods, enhanced sampling
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**Skills to omit for academia:** Excel, PowerPoint, basic office tools (assumed; wastes space).
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---
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## S5: Cover Letter Guide
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**Opening hook options (pick one):**
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- Method-development hook: "My research develops ML-guided protein engineering pipelines that compress months of experimental screening into hours, enabling rapid discovery of thermostable enzymes and high-affinity binders."
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- Scale hook: "In the past two years, I have screened over 8,500 enzyme variants using protein language models I fine-tuned, identifying 5 experimentally confirmed thermostable candidates."
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- Vision hook: "The intersection of machine learning and biomolecular simulation , where I have built my research program , aligns closely with [Department]'s strengths in [specific area]."
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**Paragraph 1 -- Research fit (3-4 sentences):**
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Connect your ML protein engineering work to the department's research strengths. Name the faculty or group if known. Reference one concrete result (e.g., 3,000x throughput, 5 confirmed hits).
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**Paragraph 2 -- Technical depth (3-4 sentences):**
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Go deeper on method development. Mention protein language model fine-tuning, transfer learning, or solvent tolerance extension. Reference the open-source tool and its adoption.
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**Paragraph 3 -- Teaching and collaboration (2-3 sentences):**
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Mention mentorship of 3 students, courses you could teach, and collaborative research plans. State what you want to do next at their institution.
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**Closing (1-2 sentences):**
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Express enthusiasm for the specific position. Reference the JD title and department name.
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**Anti-patterns:**
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- Do not restate the resume bullet-for-bullet
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- Do not begin with "I am writing to apply for..."
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- Do not use more than one exclamation mark in the entire letter
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- Do not name-drop software without saying what you did with it
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---
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*Source: experience_postdoc_lakewood.md, experience_phd_westfield.md, skills_taxonomy.md*
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