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Field
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architectures for explainable dual-process computation Design and development of deep neural network architectures and algorithms for the implementation of dual process computation approaches that improve
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the execution of the following tasks related to modelling the incineration process of the pilot units, with the following main objectives: - development of evolving algorithms based on Machine Learning
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, annotating and structuring databases, as well as feature engineering for computational modelling; d) Experience in the development and application of machine learning algorithms to the analysis of biomedical
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suitable voltage and frequency control strategies, based on state-of-the-art research, and development of dispatch algorithms for the isolated microgrid, considering the coordinated control of generation
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, inferential, and multivariate methods, including principal component analysis (PCA), regression, and machine learning algorithms (e.g., Random Forest), with the aim of integrating various environmental exposure
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(IMI, IMT, IRS, Census) with descriptive methods and causal econometric techniques. It will use various approaches to identify vacant dwellings, including machine learning algorithms to visually detect
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(IMI, IMT, IRS, Census) with descriptive methods and causal econometric techniques. It will use various approaches to identify vacant dwellings, including machine learning algorithms to visually detect
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; Develop algorithms that adjust the type of pedagogical scaffolding. The goal is always to guide the student without giving the answer, but the way of guiding will be the focus of the adaptation. Fine-tuning
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hybrid model structures for the production of biosimilars; Development of deep learning algorithms for hybrid model structures; Optimisation of reactor control based on deep hybrid models; Implementation
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: Review of the State of the Art in AI and Procurement; General system design; Development of AI algorithms and functionalities (including testing and evaluation) Preparation of project reports as