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a major challenge, accounting for up to 50% of global electricity consumption by 2030. This situation is largely due to the Von Neumann computing architecture, which limits the energy efficiency
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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-compatible measurement system using a coplanar waveguide architecture, assess their sensitivities, evaluate the possibilities of combining them, and test them with real biological samples—going beyond simple
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Institute is based on the articulation of fields of creation and research, represented by the members of its consortium: design, architecture, theatre, visual arts, digital arts, music, literature
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sources and design new energy system architectures. Among alternative fuels, hydrogen is under active investigation [3] as a promising carbon-free solution. Hydrogen combustion avoids the production
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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-fidelity neural surrogates: residual learning and hierarchical neural architectures conditioned on fidelity indicators and latent variables. - Hybrid probabilistic--deep models: combine neural
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with SQL and basic database concepts Desirable: Experience with LLMs, NLP, embeddings, semantic search, or generative AI Familiarity with RAG architectures, vector databases, or knowledge-enhanced AI
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architecture combining perception, joint control, and quantitative evaluation of collaborative performance, with the objective of improving the fluency, safety, and efficiency of collaborative mobile
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and S. Zapotoczny, Applications of surface-grafted polymer brushes with various architectures, Polym Adv Technol. 2024; 35: e6397. DOI:10.1002/pat.6397 [2] L. A. Smook, A. Dahlin, K. Schroën, and S. de