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with PV generation. The research will contribute to developing management and real-time operation tools to coordinate and optimize charging with PV production, while also enabling grid services
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an optimized extraction process for bioactive compounds from seaweed. Analyse and characterize the extracted compounds for their bioactivity and techno-functional properties. Support student supervision and
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follow quantitatively the redistribution of the phases present, reaction mechanisms will be investigated and the information gained will be used for optimization of the battery. The position is part of
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the redistribution of the phases present, reaction mechanisms will be investigated and the information gained will be used for optimization of the battery. The position is part of the INCEPTION Doctoral Network
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intelligence (AI) to optimize large dataset analysis, improving decision-making processes for awarding grants to firms and scientists. Key research areas include: Enhancing Selection Decisions: How can we make
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on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems
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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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active within the following areas: Probabilistic assessment of structures and structural elements. Including focus on uncertainty modelling of structural models and load models. Derivation of optimal
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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