{"id":4503,"date":"2025-11-24T12:09:29","date_gmt":"2025-11-24T11:09:29","guid":{"rendered":"https:\/\/www.minesparis.psl.eu\/persee\/?post_type=actualites&p=4503"},"modified":"2025-11-24T12:09:29","modified_gmt":"2025-11-24T11:09:29","slug":"phd-student-spotlight-welcoming-corrado-giancoli-to-centre-persee-mines-paris-psl","status":"publish","type":"actualites","link":"https:\/\/www.minesparis.psl.eu\/persee\/actualites\/phd-student-spotlight-welcoming-corrado-giancoli-to-centre-persee-mines-paris-psl\/","title":{"rendered":"PhD Student Spotlight \u2013 Welcoming Corrado Giancoli to Centre PERSEE \u2013 Mines Paris \u2013 PSL"},"content":{"rendered":"\n
The Centre PERSEE \u2013 Mines Paris \u2013 PSL is pleased to welcome Corrado Giancoli<\/strong>, who joined the research team on 15 October 2025<\/strong> as a PhD student. Corrado brings a multidisciplinary background spanning quantitative finance<\/strong>, machine learning<\/strong>, and quantum physics<\/strong>, positioning him uniquely to address one of today\u2019s most ambitious scientific challenges: Corrado\u2019s doctoral research, conducted under the supervision of Professor Georges Kariniotakis<\/strong> and Dr. Simon Camal<\/strong> at Centre PERSEE, and in co-direction with Professor Fei Teng<\/strong> from Imperial College London<\/em>, is titled:<\/p>\n \u201cQuantum Machine Learning and Computing Algorithms for Optimising Sustainable Energy Systems.\u201d<\/strong><\/p>\n This project investigates how quantum optimisation techniques<\/strong> and quantum-enhanced machine learning models<\/strong> can support critical power system functions, including:<\/p>\n Renewable energy forecasting<\/strong>,<\/p>\n<\/li>\n Unit commitment and operational planning<\/strong>,<\/p>\n<\/li>\n Grid operation under uncertainty and variability<\/strong>.<\/p>\n<\/li>\n<\/ul>\n The objective of the work is twofold: develop quantum-based algorithms that can be rigorously benchmarked<\/strong> against state-of-the-art classical approaches, and pave the way for their future integration into operational tools<\/strong> for energy system operators.<\/p>\n By merging expertise from optimisation<\/strong>, quantum information science<\/strong>, and energy systems engineering<\/strong>, Corrado\u2019s research aims to:<\/p>\n Enhance the robustness and efficiency<\/strong> of decision-making processes in increasingly complex energy systems.<\/p>\n<\/li>\n Improve forecasting and data-driven management<\/strong> of renewable and flexibility resources through quantum machine learning.<\/p>\n<\/li>\n Translate advances in quantum technologies<\/strong> into concrete, real-world applications supporting the energy transition.<\/p>\n<\/li>\n<\/ul>\n This interdisciplinary PhD is funded by the Institut des Transformations Num\u00e9riques (ITN) Mines Paris \u2013 PSL<\/strong> and conducted within the ERSEI Group of Centre PERSEE<\/strong>, in close collaboration with Imperial College London<\/strong>. It embodies the commitment of Mines Paris \u2013 PSL to advance research at the interface of emerging digital technologies and sustainable energy.<\/p>\n
the application of quantum computing to sustainable energy systems.<\/strong><\/p>\nAn Interdisciplinary PhD at the Crossroads of Energy and Quantum Technologies<\/strong><\/h2>\n
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Research Aims and Expected Impact<\/strong><\/h2>\n
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A Project Supported by ITN Mines Paris \u2013 PSL<\/strong><\/h2>\n