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, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and
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of AI adoption, weighing investment and operating costs against measurable returns such as reduced labor input, improved product quality, longer machine uptime, shorter lead times, and more resilient
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-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
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. Through our bachelor’s and master’s degrees, Professional Learning & Development programmes, and interdisciplinary research themes – including Emerging Technologies & Societal Transformations, Resilience