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infrastructures and cyber-physical systems with the aim to protect our most sensitive and valuable assets. We look into systems in the small and how we can prepare them to withstand and operate safely and securely
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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will be part of the Institute for Lifespan Development, Family and Culture: https://www.uni.lu/fhse-en/research-groups/institute-for-lifespan-development-family-and-culture/ Responsibilities: Obtaining a
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social acceptability. D2ET will develop a comprehensive digital platform for planning energy transition scenarios, leveraging a consolidated data model and advanced analytics to facilitate strategic
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the development of the rule of law in the Global South. Being part of the project will allow the doctoral researcher to enhance his/her knowledge-base and experience; working on the various aspects
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research at the interface between biophysics, biomedicine, and computer science Simulate molecular dynamics of ion channels Predict variant-specific protein structures using AlphaFold Co-develop machine
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The doctoral researcher will be working under the supervision of Professor Picard. The doctoral researcher’s main task will be to prepare a doctoral thesis in the field of Quantitative Urban and/or
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for Digitalization. This project aims to advance privacy-preserving techniques for data analysis and task automation, ensuring robust protection of sensitive information. The focus will be on developing
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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the society to develop alternative technologies to fossil fuels. Green hydrogen, i.e., hydrogen that is produced through electrolysis of water powered by renewable energy, has been recognized as a sustainable