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Manage project reporting together with the Reseach support team at the faculty For further information, please contact Julien Schleich, Tel. +352 46 66 44 5337, email: Your profile PhD in Artificial
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-cell interaction proteomics by proximity labeling, subcellular localization by spatial proteomics and fluorescence microscopy, and protein structure prediction by artificial intelligence and crosslinking
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. The BayesCompare project is a FNR funded project on Bayesian comparisons between artificial and natural representations to improve our understanding how natural and artificial intelligences process information
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sparse lattices, Design of Reflective Intelligent surfaces, Design of advanced radome solutions using artificial dieletrics. Knowledge and assistance on D2V, D2D, RIS-assisted NTN For further information
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APPLICATION CLOSING DATE: April 13th, 2025 The integration of Artificial Intelligence, mathematical modelling, and advanced computational simulations of biological systems is transforming biomedical
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biobanks. Project description DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods, and artificial intelligence to study biological systems and processes at all levels, from
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RIKEN's research activities. Number of openings: Around 70 Research fields: Mathematical Sciences (pure mathematics, applied mathematics, computer science, information science, artificial intelligence, etc
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Postdoc position (f_m_x) ,,Combining Physics-Based Machine Learning and Global Sensitivity Analys...
topic of induced seismicity? Then a position in this project might be appealing to you. The aim of the project is the further development and optimization of hybrid artificial intelligence systems. A
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-generation artificial phosphors and radiation dosimeters for use in the medical industry. Responsibilities and qualifications You will lead the development of a universal model framework for simulating charge
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and explainable hybrid Artificial Intelligence, i.e., the mix of formal knowledge representation and reasoning with sub-symbolic data-driven machine learning approaches, to work on car-driver digital