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on the development of methodologies and techniques of Evolutionary Computation and Machine Learning. V - Initial grant duration: 3 months V.I - Renewal Possibility: Possibily renewable VI - Funding and financial
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of Research Grants of NOVA.id.FCT – Association for Innovation and Development of FCT; Regulation of Research Grants of the Foundation for Science and Technology, I.P. in force (https://www.fct.pt/apoios/bolsas
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Development of FCT; Regulation of Research Grants of the Foundation for Science and Technology, I.P. (https://www.fct.pt/apoios/bolsas/regulamento.phtml.pt ); in force. Where to apply E-mail df.semedo
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: Hydrology, Water Resources, Data Science, and Computing Admission requirements: BSc or MSc degrees in one of the following areas: Civil Engineering, Computer Engineering, Environmental Engineering, Physical
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of Research Grants of NOVA.id.FCT – Association for Innovation and Development of FCT; Regulation of Research Grants of the Foundation for Science and Technology, I.P. in force (https://www.fct.pt/apoios/bolsas
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Engineering or Industrial Engineering and Management) - 10 points; Others Masters – 2 points) b) Experience in applying machine learning algorithms, data preparation, normalization, feature selection, and
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application of statistical Machine Learning tools. WORK PLAN Collaborate in the following tasks of the project: a) Contribute to the design and development of the Life Cycle Assessment (LCA) system; b) Support
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–5): selection of relevant climatic variables and application of statistical modelling and/or machine learning techniques to predict risk. 3) Preliminary validation of the predictive model using
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 10 days ago
. The candidate(s) may also be required to apply data fitting algorithms/machine learning algorithms to link models to biological data from the literature. The project integrates elements from dynamical systems
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hematology ● Knowledge in clinical analysis ● Knowledge in statistics ● Knowledge and experience in handling biological samples ● Knowledge of activities inherent to AEQ programs ● Good computer skills from