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natural and human disturbances through climate-smart forestry startegies, based on observations and predictive models. Where to apply Website https://unimol.concorsismart.it/ Requirements Additional
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institutes and an industry partner. Your task is the build-up of a predictive model for tandem cell stability. Your tasks in detail: You receive tandem solar cells from a partner institute and perform high
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of the SU(N) Fermi-Hubbard model and its low-temperature phases at the microscopic level. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1003
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understanding of the underlying physical mechanisms and to leverage this knowledge to develop predictive tools for optimizing the design and control of wind farms. Research scope and responsibilities Depending
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algorithmic breakthroughs that enable foundation models to run predictably and efficiently on embedded processors and accelerators. FIND is a research program funded by the Dutch government and industry
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other machine learning models. Generate and evaluate hydrologic hindcasts and forecasts to assess model fidelity, forecast reliability, and predictive skill across subseasonal to annual time scales
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the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning modelling. This exciting project involves the application of innovative
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complex, high dimensional and high-volume datasets. Uses data preparation, modeling and predictive modeling, analysis, processing, algorithms, and systems. Applies knowledge of statistics, machine learning
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project working to develop real-time vector-borne disease risk assessment in low resource areas. The individual will be directly responsible for the development of adaptive predictive models for nowcasting
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prediction, focusing on efficient edge deployment (e.g., through model pruning, quantization, or TinyML techniques). The embedded system will be designed to perform local inference in real-time, minimizing