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identified in other WPs. The model construction will be informed by qualitative and quantitative knowledge of supply chain processes through dialogue with stakeholders, and model parameters will be estimated
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through long-term impact assessment and optimization. The goal is to develop a framework to estimate carbon emissions across AI's development, operation, and use. This framework enables stakeholders
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becomes essential. This project will focus on building a comprehensive digital twin of a future quantum computer to investigate how classical subsystems scale and interact, and how this scaling impacts
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challenge, making energy-efficient computing a critical research priority. This project addresses this challenge through a novel co-design approach that simultaneously optimizes both hardware and software
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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applicants for a 6-month paternity leave replacement who have a strong interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental
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in at least two projects related to scale up and optimization of biomass valorization processes from wood and agriculture feedstocks for textiles and packaging applications. The portfolio of projects
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inland, short-sea, and high-seas shipping routes. The project seeks to deliver industry-relevant tools that enable optimal design and operation of greener vessels, backed by real-world demonstrations
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national and international partners Maintain, calibrate and troubleshoot high-end mass spectrometers with a focus on GC-HRMS and Orbitrap analysers Develop, optimize, and validate analytical GC-MS and LC-MS
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been shown to accelerate and improve the training procedure of SNNs by defining new cost functions that are differentiable and easier to optimize. They can also handle quantized weights, e.g., using