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Field
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, and climate projections depends critically on the adequate representation of land-atmosphere (L-A) feedbacks. These feedbacks are the result of a highly complex network of processes and variables
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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develop numerical models and Machine Learning and AI methodologies, including Physics Informed Neural Networks (PINNs) and Symbolic Regression tools, to predict chemical reactions, impurity evolution along
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the effects of these factors at the tissue level. The position will also involve simulating and optimizing complex models that integrate biological and physical data using advanced methods, such as physics
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart
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immunotherapy, transplant rejection and autoimmunity. The objective of the LIT is to develop efficient immune therapeutic strategies in these areas. Close networking between University and University Hospital
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of nonlinear optics through the integration of artificial intelligence. The successful candidate will lead projects that combine the development of Physics-Informed Neural Networks (PINNs) with advanced fiber
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, transformers, graph neural networks, fine-tuning techniques, and inference and optimization. Writing academic reports Delivering high-quality work at a fast pace, understanding the importance of time and speed
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that integrate physical constraints (Physics-Informed Neural Networks), as well as for the implementation and optimization of the associated algorithms. The researcher will analyze and interpret experimental and