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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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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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Applications should include: Curriculum Vitae Cover letter Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered. The University of Luxembourg is committed to...
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parameter set for quantum internet tasks. Is Your profile described below? Are you our future colleague? Apply now! Education · MSc degree in physics, quantum technology, materials science, or
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physical SAM. While INRIA Lille leads the control design, both teams will collaborate on use-case scenarios and real-world demonstrations to assess performance and future potential. The successful candidate
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relevant state-of-the-art technologies. S/He will benefit from an active seminar program, international conference attendances, opportunities for professional growth. The project will be carried out in
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diverse backgrounds (e.g., economics, engineering, computer science, information systems, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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, Large Language Models, Human-Computer Interaction, Virtual reality. The selected candidate will work on the design and implementation of a human-computer interface to support education using an AI-based
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curiosity, innovation and entrepreneurship in all areas Personalized learning programme to foster our staff’s soft and technical skills Multicultural and international work environment with more than 50
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Pathogenesis in the age of the microbiome (MICRO-PATH; https://micro-path.uni.lu ) is a highly competitive, interdisciplinary, research-intensive PhD training programme, supported by the PRIDE