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compositions. Initially, supervised learning models such as random forests, gradient boosting, and neural networks will be used to predict composition outcomes based on both literature scrapping and in-house
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techniques and neural network techniques to adjust high-resolution X-ray spectra and infer physical properties of the emitting plasma. · Developing algorithms that optimise the adjustment of high-resolution X
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part of an international network of 13 research labs located throughout Europe who work on the EU funded HUM.AI.N-ACCENT project. The full consortium count with a total of 24 academic and non-academic
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of this annex, as well as to: Programming in Python and R. Statistical classification and machine learning methods: SVM, neural networks and logistic regression. 3.2. Qualification: Official Master’s degree in