Welcome to my GitHub! I'm a computational physicist with a strong foundation in scientific programming, data analysis, and machine learning — now channeling my expertise toward becoming a data scientist.

• 📫 How to reach me: [email protected]
• 💼 LinkedIn
• 💬 Always happy to collaborate, learn, and explore new ideas in data, science, and code.

• 🎓 Bachelor's in Physics – UFPA (2011)
• 🎓 Master's in Physics – UnB (2016)
• 🎓 PhD in Physics – UnB (2021)

My academic research has focused on:

• Computational atomic and molecular physics
• Electronic transport in organic semiconductors
• Quantum chemistry and DFT simulations

• Python (numpy, scipy, pandas, matplotlib)
• Fortran (legacy and modern codebases)
• Bash scripting
• LaTeX (for scientific documentation)
• Wolfram Mathematica

• Data wrangling and visualization
• Machine learning fundamentals (scikit-learn, TensorFlow basics)
• Web scraping and automation
• Experience with MySQL and relational databases

• DFT simulations (Gaussian, Q-Chem)
• High-performance computing (HPC)
• Linux cluster setup and management (SLURM, SSH)

I'm currently focusing on:

• Expanding my machine learning and data science toolkit
• Building and contributing to open-source projects
• Applying my analytical and programming skills to real-world datasets

🔗 cammneto/Stock-Screener-bovespa
This Python toolkit scrapes and aggregates financial data for all stocks listed on B3 (São Paulo Stock Exchange) from four platforms—Status Invest, InvestSite, Investidor10, and Fundamentus—and exports clean, date‑stamped CSVs for downstream analysis.

Key Features

• Multi‑source data aggregation across Status Invest, InvestSite, Investidor10, and Fundamentus
• Automated sitemap parsing for dynamic discovery of stock pages
• Customizable scraping parameters and modular, scalable design
• CSV export with standardized naming for seamless integration into analytics pipelines

🔗 cammneto/carrier-mobility
A Python package to estimate electronic carrier mobility in organic semiconductor dimers by parsing Gaussian09 .log outputs and applying both Marcus–Hush and Marcus–Levitch–Jortner models.

Highlights

• Reads displacement vectors and transfer integrals from Gaussian09 outputs for any dimer system
• Implements Marcus–Hush and Marcus–Levitch–Jortner formalisms for accurate mobility estimation
• Modular helper utilities in cmtools.py for preparing inputs and processing results
• Includes example ethylene dimer workflow to illustrate setup and reproducibility

Thanks for stopping by! 👨‍💻