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Field
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computer vision algorithms to detect clinical interventions performed by nurses and situations of agitation and risk of falling. Volume of data available for the project: Video capture in a hospital
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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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(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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referred to in subparagraph e) of paragraph 3 of Article 4 of Decree-Law No. 74/2006, in its current wording, provided that, under subparagraph e) of Article 3 of RBI-FCT, they are developed in association
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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
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for hiring PhD graduates aimed at stimulating scientific and technological employment in all areas of knowledge, hereinafter referred to as RJEC, with a view to developing research activities within the scope
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Research Fellowship for a Master under the project New Generation Storage - NGS, REFª C644936001-00000045, financed under Invitation Notice n.º 02/C05-i01/2022 regarding the development of projects within