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    "name": "LAMPROS PAPAIOANNOU",
    "label": "ELECTRICAL AND COMPUTER ENGINEER",
    "summary": "Final-year Electrical and Computer Engineering student at the National Technical University of Athens (NTUA), specializing in Energy Systems. I combine the rigorous mathematical and analytical foundation of an engineering degree with practical skills in Python and Machine Learning. Passionate about applying AI techniques to energy infrastructure, with a strong aptitude for reverse-engineering complex systems and codebases to deliver data-driven solutions.",
    "location": "Athens, GREECE"
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  "education": [
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      "institution": "National Technical University of Athens (NTUA)",
      "degree": "Master of Science",
      "field": "Electrical and computer engineering",
      "startDate": "2018",
      "current": true,
      "score": "72,9%",
      "highlights": [
        "Expected Graduation: September 2026",
        "Specialization: Energy Systems",
        "elevant Coursework: Control Systems, Electric Power Systems, Algorithms & Data Structures, Statistics, Systems Theory."
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    {
      "institution": "Massachusetts Institute of Technology (MIT)",
      "degree": "MIT Learn Certificate",
      "field": "Universal AI Course",
      "startDate": "2026",
      "current": true,
      "highlights": [
        "I am currently taking the Universal AI course via MIT learn, that provides a certificated focusing on advanced Machine Learning architectures, generative models, AI-driven system optimization and hands-on application of AI frameworks to solve real-world problems."
      ]
    }
  ],
  "skills": [
    "CAD/CAM & Simulation: Fusion 360, ANSYS, PanelCAD",
    "Tools/Other: Git, GitHub, VS Code, Linux/Bash, LaTeX",
    "AI/ML: Machine Learning Fundamentals, Data Analysis, Neural Networks",
    "Engineering: Power Systems Analysis, Mathematical Modeling, Circuit Design",
    "Programming: Python (NumPy, Pandas, Scikit-learn, Matplotlib)",
    "Problem Solving",
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    "Reverse Engineering Mindset",
    "Technical Adaptability",
    "Analytical Reasoning"
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      "fluency": "Native"
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      "fluency": "Advanced"
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    {
      "name": "Diplomatic Thesis",
      "publisher": "AI-Driven Performance Prediction in Hydrogen Fuel Cells",
      "summary": "Designed and implemented a Neural Network model to predict the efficiency and performance of hydrogen fuel cells. Performed end-to-end data preprocessing, cleaning, and feature engineering on experimental datasets to optimize model training in a Python environment. Conducted rigorous analysis of model outputs to determine optimal operational parameters for maximum system efficiency. Key Competency: Successfully bridged the gap between physical phenomena (Hydrogen Fuel Cells) and algorithmic modeling."
    },
    {
      "name": "Medical AI",
      "publisher": "Parkinson’s Disease Symptom Detection",
      "summary": "Developed a Neural Network in Python trained on PubMed medical datasets to identify Parkinson’s symptoms, focusing on data preprocessing and model accuracy."
    },
    {
      "name": "Electrical & Physical Simulation",
      "summary": "Signal Analysis: Conducted wave propagation simulations using MATLAB Ray Tracing. Professional Design: Applied PanelCAD for the technical design and documentation of electrical installations, ensuring compliance with industry standards."
    }
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