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is focused on enabling applications through a fundamental understanding of material formation processes and properties. KU Leuven (University of Leuven) has been a center of learning for nearly six
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distribution of active sites and guide future catalyst design. Structural insights will be correlated with catalytic performance metrics, provided by project partners, to identify the role of material structure
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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onboarding period that includes specialized courses and hands-on training in AI and machine learning. You'll also have the chance to explore different labs and core facilities, meet fellow researchers, and
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, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role
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interest to acquire those in exercise physiology, metabolic & inflammatory regulation, molecular biology and biochemistry. Experience with basic molecular biology techniques, such as protein/mRNA extraction
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, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role
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. The central aim is to design intelligent systems that dynamically adapt the environment to support optimal learning conditions, based on real-time neurophysiological feedback. Key principles guiding
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(preferably) in Civil Engineering-Architecture, Civil Engineering or equivalent degree. You enjoy performing research. You are independent, self-motivated and eager to learn. Experience and/or a master thesis
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. Digital Extraction from Historical Taxonomic Literature Application of OCR and machine learning algorithms to digitize printed and handwritten documents; Linking specimen mentions in literature to digital