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programme data, and satellite observations in Meteorology/Earth Sciences (0-5 points); (ii) Experience with meteorological and air quality observation/monitoring station data (e.g. IPMA station network
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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms
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Learning (CNN, RCNN), Large-Scale Language Models (e.g., transformer-based models such as Llama), and Statistical Network Models. The work also includes the writing of technical reports and scientific
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science/AI; Implementation methodologies of Big Data in organizational networks or ecosystems; Methods for improving business processes and operational performance, based on data on innovation and
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neural networks, enabling us to estimate the reliability of a single decision of this algorithm. Regarding generalisation, recent self-supervised learning paradigms have strong synergies with the multi
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. Functional testing of developed PCBs, including experimental validation of electronic circuits and S-parameter characterization using a vector network analyzer (VNA).; 4. Experimental characterization