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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation
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learning models on wearable electronic circuits, devices, and platforms, with particular emphasis on smart eyewear. The research activities will address multiple application domains, including embedded
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, designing, implementing, and evaluating ML models that address practical challenges across domains. The researcher will contribute to the development of a full machine learning pipeline, including data
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illumination variations, which introduce non-stationary shifts and degrade the performance of conventional models. The project proposes the use of hypernetworks to dynamically adapt the parameters of the gaze
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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using machine learning methodologies; (2) the extension of an existing CFD framework for multiphase modeling to the case of PEC systems; (3) the implementation within the framework of a description of
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machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
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: Dep.of Ingegneria Duration: 12 months Where to apply Website http://www.unife.it Requirements Additional Information Eligibility criteria Eligible destination country/ies for fellows: Italy Eligibility
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Development of innovative experimental model systems for mechanistic investigation and translational validation of microbiome-mediated processes Advanced AI and machine learning frameworks for integrative multi
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of Business Intelligence (machine learning & LLM) to enhance the sustainability of regional tourism. On the supply side, a monitoring system is developed by extracting data from online platforms such as Google