Akhil Reddy Peeketi
c51b49882f
Complete AI-assisted resume/CV generation framework: - 6 Claude Code skills (setup-extract, setup-build-kb, make-resume, make-cl, edit-resume, critique) - LaTeX templates (resume, CV, cover letter) with .cls class files - 6 reference docs (shared_ops, resume_reference, cl_reference, critical_rules, session_file_template, critique_framework) - Fictional Dr. Jordan Chen examples (extraction, experience, bundle, config, session, JD) - Knowledge base scaffolding and config template - README with setup guide and workflow documentation
119 lines
6.3 KiB
Markdown
119 lines
6.3 KiB
Markdown
# Bundle: Academia
|
|
|
|
> Role-type positioning guide for university faculty and research professor positions.
|
|
|
|
---
|
|
|
|
## S1: Role Profile
|
|
|
|
**Target employers:** R1 research universities, liberal arts colleges with research programs, international universities
|
|
**Typical titles:** Assistant Professor, Associate Professor, Research Assistant Professor, Lecturer, Postdoctoral Fellow
|
|
**What they value (ranked):**
|
|
1. Independent research capability with publication record
|
|
2. Teaching experience or potential
|
|
3. Method development (not just method application)
|
|
4. Cross-disciplinary breadth (computational + experimental collaboration)
|
|
5. Mentorship and advising evidence
|
|
6. Grant-writing experience or potential for external funding (NIH, NSF)
|
|
7. Open-source contributions and community engagement
|
|
|
|
**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).
|
|
|
|
**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.
|
|
|
|
---
|
|
|
|
## S2: Summary Guide
|
|
|
|
**Tagline pattern:** [Method developer] + [application domain] + [scale/impact metric]
|
|
|
|
**Building blocks (pick 3-4 for summary):**
|
|
- ML-guided protein stability prediction (ESM-2, transfer learning)
|
|
- High-throughput virtual screening (8,500+ enzyme variants)
|
|
- Transfer learning for low-data protein property prediction
|
|
- Enhanced sampling MD (metadynamics, replica exchange, FEP)
|
|
- Enzyme solvent tolerance prediction
|
|
- Open-source tool development (200+ GitHub stars)
|
|
- Automated screening pipeline (Snakemake, SLURM)
|
|
- Consistent domain: enzyme engineering, protein stability, folding thermodynamics
|
|
|
|
**Summary do's:**
|
|
- Open with "Computational biologist" or "Protein engineer"
|
|
- Include one quantified throughput/scale metric
|
|
- Name 2-3 specific methods/tools
|
|
- Close with a research vision statement
|
|
|
|
**Summary don'ts:**
|
|
- Do not open with "Passionate" or "Motivated"
|
|
- Do not list more than 3 software tools in the summary
|
|
- Do not use buzzwords without concrete backing ("cutting-edge", "novel", "innovative")
|
|
|
|
---
|
|
|
|
## S3: Achievement Reframing Map
|
|
|
|
**Priority matrix for academic roles:**
|
|
|
|
| Priority | Achievement | Why | Reframing Notes |
|
|
|----------|------------|-----|-----------------|
|
|
| 1 (must) | L1: Enzyme Stability Screening | Core ML pipeline development + high-impact application | Lead bullet. Emphasize 3,000x throughput and independent development. |
|
|
| 2 (must) | L4: Transfer Learning Framework | Open-source impact, community adoption | Highlight GitHub stars and external adoption as evidence of research maturity. |
|
|
| 3 (must) | L3: Automated Screening Pipeline | Infrastructure contribution, team enablement | Frame as "enabling 6 researchers" -- departments value force multipliers. |
|
|
| 4 (strong) | L2: Enzyme Solvent Tolerance | Deeper enzyme engineering expertise | Natural extension of stability work into industrial conditions. Note under-review status. |
|
|
| 5 (strong) | L5: Unfolding Pathway Analysis | Mechanistic insight from simulations | Use if JD mentions dynamics, thermodynamics, or structural biology. |
|
|
| 6 (if room) | L6: Mentorship | Teaching and advising fit | Include for faculty positions; optional for postdoc applications. |
|
|
|
|
**Omit from academic resumes:** Undergraduate coursework projects, non-research achievements.
|
|
|
|
---
|
|
|
|
## S4: Skills Guide
|
|
|
|
**Bold tools (tools the JD will likely name or ATS will scan):**
|
|
- **GROMACS**, **Python**, **PyTorch**, **SLURM**
|
|
- **Machine learning** (or **protein language models** if JD uses that phrase)
|
|
|
|
**Include but do not bold:**
|
|
- AlphaFold2, Rosetta, OpenMM, RDKit, BioPython, MDAnalysis
|
|
- Snakemake, Git, Bash, PostgreSQL, Linux
|
|
|
|
**Group strategy (for skills section):**
|
|
- Group 1 -- Simulation & Modeling: GROMACS, OpenMM, AMBER, AutoDock Vina
|
|
- Group 2 -- Machine Learning: Protein language models (ESM-2), graph neural networks, transfer learning, PyTorch
|
|
- Group 3 -- Programming & HPC: Python, Bash, SLURM, Snakemake, Git
|
|
- Group 4 -- Analysis & Visualization: BioPython, MDAnalysis, ProDy, PyMOL, matplotlib
|
|
- Group 5 -- Domain Knowledge: protein engineering, drug discovery, free energy methods, enhanced sampling
|
|
|
|
**Skills to omit for academia:** Excel, PowerPoint, basic office tools (assumed; wastes space).
|
|
|
|
---
|
|
|
|
## S5: Cover Letter Guide
|
|
|
|
**Opening hook options (pick one):**
|
|
- 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."
|
|
- 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."
|
|
- 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]."
|
|
|
|
**Paragraph 1 -- Research fit (3-4 sentences):**
|
|
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).
|
|
|
|
**Paragraph 2 -- Technical depth (3-4 sentences):**
|
|
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.
|
|
|
|
**Paragraph 3 -- Teaching and collaboration (2-3 sentences):**
|
|
Mention mentorship of 3 students, courses you could teach, and collaborative research plans. State what you want to do next at their institution.
|
|
|
|
**Closing (1-2 sentences):**
|
|
Express enthusiasm for the specific position. Reference the JD title and department name.
|
|
|
|
**Anti-patterns:**
|
|
- Do not restate the resume bullet-for-bullet
|
|
- Do not begin with "I am writing to apply for..."
|
|
- Do not use more than one exclamation mark in the entire letter
|
|
- Do not name-drop software without saying what you did with it
|
|
|
|
---
|
|
|
|
*Source: experience_postdoc_lakewood.md, experience_phd_westfield.md, skills_taxonomy.md*
|