Efosa Enomayo

Electrical Engineering graduate with hands-on experience in automation, analog design, and embedded systems. Skilled in Python, VHDL, and C++, with practical expertise developing scalable EDA workflows and data extraction pipelines that improve efficiency and reliability. Bilingual in English (native) and German (C2). Motivated to apply technical knowledge and problem-solving skills in engineering and data-driven innovation roles.

Bachelor's Thesis: Structured Analysis of Research Data for Analog Design
TUM – Chair of Electronic Design Automation, Munich | Jan 2025 – July 2025

• Built a Python-based system using deep learning (MinerU) and machine learning (GROBID) to extract structured elements (titles, figures, tables, references) from PDFs.
• Processed 3,400+ IEEE papers with GPU-accelerated parallel evaluation, achieving >0.90 F1-score.
• Created scalable, machine-readable datasets supporting analog design research.

Engineering Internship - Localization and Accumulation of Research Data for Analog Design.
TUM – Chair of Electronic Design Automation, Munich | Aug 2024 – Dec 2024

• Automated the collection of 100,000+ analog design research papers from academic platforms.
• Developed Python workflows with Selenium and URL logic for reliable, human-like data acquisition.
• Integrated into departmental infrastructure, reducing preparation time and enabling reproducible automation.