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5 Feb 2026 Job Information Organisation/Company FEUP Department Human Resources Division Research Field Engineering » Electrical engineering Engineering » Computer engineering Engineering
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value candidates with interest and/or experience in the following areas: a) Understanding of machine learning techniques, with interest in exploring algorithms such as regression, decision trees, Random
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to monitoring cracking in concrete bridges and viaducts through computer vision The work is supported by BRISA and will use as case study the viaduct over the Rio de Anços on the A1. Duration: 6 months Maximum
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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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(e.g., C++, Unity, Python) a background or interest in human-computer interaction, gender studies, and/or construction familiarity with qualitative and quantitative research methods. How to apply We
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EXPERIMENTAL, NUMERICAL AND MACHINE LEARNING”, funded by the “Programme, Innovation and Digital Transition (Compete 2030), European Regional Development Fund FEDER and national funds, Portugal 2030, Foundation
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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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machine-learning methods for sample segmentation and classification. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The fellow will join the INESC TEC team within the LIBScan project, carrying
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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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Sciences, in the frame of the R&D project Soil O-Live - The Soil Biodiversity and Functionality of Mediterranean Olive Groves: A Holistic Analysis of the Influence of Land Management on Olive Oil Quality and