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-472 Is the Job related to staff position within a Research Infrastructure? No Offer Description The field of combinatorial optimization is concerned with developing generic tools that take a
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. This intervention constitutes optimal timing and a comprehensive design to achieve its intended goals, i.e., improved functional recovery post-TJA and general health, and decreased socio-economic burden
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-constrained devices such as wearables, smart sensors, hearables, and IoT nodes. While current deployment methodologies can optimize models before deployment, the resulting software remains static throughout
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demands of real-time, edge-based processing. We are looking for a highly motivated PhD researcher with an interest in optimizing AI models on resource-constrained embedded hardware. Requirements include
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range of topics in numerical analysis and applied mathematics, including: Approximation Theory & Numerical Integration Mathematics for Data Science & AI Combinatorial Optimization Stochastics
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optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities
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together with other energy assets, such as electrical boilers, within a Model Predictive Control (MPC) framework that optimally balances electricity and heat production. Within FLEX-SMR, this PhD focuses