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models to be trained locally on devices, with only model updates (not raw data) being communicated, thus improving efficiency and privacy [14]. • Resource-Aware Scheduling: Design algorithms to optimize
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will result in novel algorithms for dynamic and context-aware scheduling that preserve timing guarantees while improving performance and resource utilization. More broadly, the thesis aims to open a new
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improving energy efficiency and privacy [14]. - Resource-Aware Scheduling: Design algorithms to optimize task placement (edge vs. cloud) and scheduling policies for AI workloads, balancing latency, energy
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is mandatory to apply. Large-scale data science workloads are increasingly constrained not by algorithmic complexity or model architecture, but by the physical limits of memory hierarchies and
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(2023), pp. 1133–1184. Work plan The thesis is due to start in autumn 2026, and is expected to last 36 months. More specifically, it will be structured as follows (indicative schedule): • Defining
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The candidate must have a strong background in algorithmics, optimization, and data science. In particular: Mathematical modelling: ability to formalize real-world personnel scheduling problems, including
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for Artificial Intelligence. The position aims to strengthen methodological and algorithmic work on one or more of the following axes: Optimization and learning on very large models and datasets, improving
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
currently relies solely on the surgeon’s expertise [2]. Unlike scheduled orthopedic procedures, trauma surgery has seen little integration of artificial intelligence in preoperative planning. Currently