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into the wave interaction and propagation processes. Modelling the propagation characteristics of optical communication systems with a focus on optical atmospheric turbulence and statistical prediction models
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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-package of the ChiExCo program, which aims to develop a reliable computational protocol to predict, for organic chromophores, both chirality quantifying factors (gabs and glum) resulting from excitonic
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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exploratory analysis on large, multi-dimensional datasets; (b) develop predictive/diagnostic models and algorithms to lead and support clinical/translational research; (c) collaborate with cross-functional
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interventions can promote cognitive abilities in aging. The main task is to develop methods for predicting health outcomes using dynamic and adaptive modeling whilst addressing computational challenges
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will focus on designing computationally efficient, scalable, and adaptive AI models that operate under strict constraints in radio access, edge, and non-terrestrial network environments. The position is
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processes, targeting annual savings of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real
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, CRCF). It will develop AI tools to map and predict soil health across space and time, accelerate literature reviews, extract best management practices from long-term experiments, and design methods
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regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty bounds and deriving