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I am a Ph.D. candidate in Industrial Engineering at Università degli Studi di Napoli “Federico II” (2022–2025), specializing in advanced analytics and sensor data monitoring. My research and industry collaborations with Hitachi Rail STS and Perpetuum Onboard (UK) have centered on applying statistical methods to battery and vibration data, enabling early fault detection and performance optimization across railway fleets. Through these projects, I have developed strong expertise in Python-based data analysis, system monitoring, and maintenance optimization, while honing skills in teamwork, time management, and client-supplier communication. I aspire to contribute to innovative, results-oriented projects that integrate engineering and statistical competencies to deliver continuous improvement and optimal solutions.…I am a Ph.D. candidate in Industrial Engineering at Università degli Studi di Napoli “Federico II” (2022–2025), specializing in advanced analytics and sensor data monitoring. My research and industry collaborations with Hitachi Rail STS and Perpetuum Onboard (UK) have centered on applying statistical methods to battery and vibration data, enabling early fault detection and performance optimization across railway fleets. Through these projects, I have developed strong expertise in Python-based data analysis, system monitoring, and maintenance optimization, while honing skills in teamwork, time management, and client-supplier communication. I aspire to contribute to innovative, results-oriented projects that integrate engineering and statistical competencies to deliver continuous improvement and optimal solutions.

Emanuele Rossi

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I am a Ph.D. candidate in Industrial Engineering at Università degli Studi di Napoli “Federico II” (2022–2025), specializing in advanced analytics and sensor data monitoring. My research and industry collaborations with Hitachi Rail STS and Perpetuum Onboard (UK) have centered on applying statistical methods to battery and vibration data, enabling early fault detection and performance optimization across railway fleets. Through these projects, I have developed strong expertise in Python-based data analysis, system monitoring, and maintenance optimization, while honing skills in teamwork, time management, and client-supplier communication. I aspire to contribute to innovative, results-oriented projects that integrate engineering and statistical competencies to deliver continuous improvement and optimal solutions.…I am a Ph.D. candidate in Industrial Engineering at Università degli Studi di Napoli “Federico II” (2022–2025), specializing in advanced analytics and sensor data monitoring. My research and industry collaborations with Hitachi Rail STS and Perpetuum Onboard (UK) have centered on applying statistical methods to battery and vibration data, enabling early fault detection and performance optimization across railway fleets. Through these projects, I have developed strong expertise in Python-based data analysis, system monitoring, and maintenance optimization, while honing skills in teamwork, time management, and client-supplier communication. I aspire to contribute to innovative, results-oriented projects that integrate engineering and statistical competencies to deliver continuous improvement and optimal solutions.

Available to hire

I am a Ph.D. candidate in Industrial Engineering at Università degli Studi di Napoli “Federico II” (2022–2025), specializing in advanced analytics and sensor data monitoring. My research and industry collaborations with Hitachi Rail STS and Perpetuum Onboard (UK) have centered on applying statistical methods to battery and vibration data, enabling early fault detection and performance optimization across railway fleets. Through these projects, I have developed strong expertise in Python-based data analysis, system monitoring, and maintenance optimization, while honing skills in teamwork, time management, and client-supplier communication. I aspire to contribute to innovative, results-oriented projects that integrate engineering and statistical competencies to deliver continuous improvement and optimal solutions.