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interest in the following relevant fields: machine learning and cybersecurity. While robust infrastructures now exist for running cybersecurity exercises, the creation and management of training scenarios
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) position in Computational Fluid Dynamics. Advances in computational sciences, machine learning, and artificial intelligence are transforming the study of complex flows. Such systems are prevalent in both
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, and Machine Learning/AI 5G on organisations from both the private and public sectors
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photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning, and theoretical analysis using Leslie-Ericksen
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, GenAI, and IoT/5G on organisations from both the private and public
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variants of the sodium channel Nav1.1, which are associated with different forms of epileptic syndromes and migraine. The aim of the project is to use machine-learning assisted molecular dynamics simulations
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We seek a highly motivated bioinformatician or computational biologist who is well versed in the statistical and machine learning analysis of biomedical data and bioscientific programming for a
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electrical engineering, automation, computer engineering, or a closely related discipline, with proven expertise in the operation and control of intelligent energy systems. Experience and skills Solid
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We are seeking a highly motivated PhD candidate with a strong interest or background in AI as well as in one or more of the following areas: Generative AI, Natural Language Processing, Deep learning