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programming, and/or human-computer interaction (UX), and/or application of Machine Learning. Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. 3. Financial
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on scientific projects. Posts on machine learning topics in unbalanced domains. 4. Work Plan: 4.1. The purpose of this contract is to perform the following tasks: Definition of the methodology for Natural Capital
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for 1 research grant within the framework of project “Understanding Machine Learning Systems”, financed by Faculdade de Engenharia da Universidade do Porto, under the following conditions: Scientific Area
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Engineering Department at ISEP; Substitute member – Doctor Maria Eduarda da Rocha Pinto Augusto da Silva, Professor at the Faculty of Economics of the University of Porto; Substitute member – Doctor Pedro
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 12 days ago
networks or similar machine learning technologies applied to DNA; Preferential: Experience with transcription factor motif discovery; Proficiency in high-throughput sequence alignment methods; Candidates who
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 7 days ago
-throughput screening; - Cell painting assays and high-content image-based analysis (e.g., CellProfiler, Harmony); - Machine learning models for antimicrobial activity prediction (e.g., Weka); - Strong
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“Enhancing Machine Learning Approaches for Spatially Dependent Data in Fisheries and Environmental Research” (CMAT, University of Minho), reference 2024.15617.PEX, financed by national funds through
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computer vision to analyze photos and thermal images. Objectives include data collection, improving available degradation models, new machine learning-based classification and risk assessment methods, and
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2022.09373.PTDC financed by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning/Recommender Systems Admission requirements: Candidates who cumulatively meet the
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Grant(s) (RG) in the scope of R&D projects FireLSF - Development of predictive models for the fire resistance of light steel frame walls - an integrated experimental, numerical and machine learning