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Aurelian-Ionut Dodis is a Ph.D. student at the University of Southern Denmark, specializing in Engineering Operations Management. His research focuses on Predictive Maintenance (PdM) and Condition Monitoring (CM) within the Maritime Industry. Aurelian's expertise includes programming tools and technologies that leverage Machine Learning algorithms to enhance operational efficiency and predict equipment wear. He is currently involved in active projects, including the ShippingLab II initiative, which aims to develop digital twin technologies for engine condition monitoring and wear prediction. Aurelian has a strong interest in employing advanced data analytics to generate actionable insights and improve service usage across various marine applications.
University of Southern Denmark • Sønderborg, Denmark
Engaged in research and development in Engineering Operations Management, focusing on predictive maintenance and condition monitoring in the maritime sector.
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