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to optimize performance and interpretability, analogous to RAG (Retrieval-Augmented Generation) in LLMs Investigating multiple models for analysis, focusing on the Occam’s Razor principle of preferring simpler
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funded PhD scholarships (3-year employment) in a vibrant interdisciplinary research environment. The positions are part of the research project “AI-driven materials optimization for light trapping in thin
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
feedback, and real-time agent-based simulation for guiding optimal work performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human behavior and provides active
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
for realistic haptic control and feedback, and real-time agent-based simulation for guiding optimal work performance. Following smart serious gaming approaches, novel artificial intelligence forecasts human
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CMOS analog/mixed-signal circuits (Cadence, Spectre, etc.) Optimize circuits for power, noise, and area constraints Prototype and validate circuits with EEG signals and wearable sensors Collaborate with