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development", project number S1/4.6/E0050, financed by the European Regional Development Fund (ERDF), through the Interreg VI-B Sudoe Programme 2021-2027, under the following conditions: Scientific Area
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call for awarding a part-time (50%) research fellowship (RF) for the development of the platform DB-HERITAGE – Database of construction materials of historical and heritage interest. 3 – Financing source
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studies, technical specifications, modelling and system design. The work to be developed involves continuous analysis of the state of the art, the definition and specification of technical and functional
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intelligence algorithms.”, financed by National public entities (IFAP IP), under the following conditions: Scientific Area: Geospatial Engineering Admission requirements: Candidates must meet the following
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activities will be developed in the Power Electronics area, focusing on programming control algorithms on a Xilinx FPGA. This project aims to implement internal fault tolerance in a power electronics
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requirements: Presentation of the academic qualifications and/or diplomas, if applicable. Enrolment in Master’s in Informatics Engineering. Work plan: The work consists of the development and implementation
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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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: ………………………………………………………………….………………………………… The selected research fellow will: Design and implement randomized field experiments and/or quasi-experimental studies in collaboration with external partners such as IEFP; Contribute to the development
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), on February 21st, 2024. These Doctoral Research Fellowships are intended for candidates who meet the necessary conditions to enrol in any NOVA’s Doctoral Programme, and who wish to develop research activities
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experiments and/or quasi-experimental studies in collaboration with external partners such as IEFP; Assist in the development or application of machine learning models, particularly those focused on causal