151 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" Master positions in Portugal
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stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. Previous experience in machine learning applied to dynamic systems or orbital
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BarcelosStreetCampus do IPCA, Vila Frescaínha S. MartinhoGeofield Contact State/Province Barcelos City Barcelos Website http://www.ipca.pt Street Campus do IPCA, Vila Frescaínha S. Martinho Postal Code 4750-810 E-Mail
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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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for the exceptions expressly set out in nos. 3 and 4 of Article 5 of the referred Statute. (https://www.fraunhofer.pt/content/dam/portugal/fhp/careers/grants/Regulamento_Bolsas_Fraunhofer_Portugal_Research_2021.pdf
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amount: The fellowship amount is €1040.98, in accordance with the table of fellowship amounts directly awarded by the FCT, I.P. in Portugal (https://www.fct.pt/apoios/bolsas/valores ). The fellowship will
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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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: https://app.apply.up.pt Ref. BOLS-FFUP-26-4. 11.3. The application must be accompanied by documents proving the conditions set out in this Notice, namely: a) Curriculum vitae, indicating full name
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. VII.VIII - All provisional and final jury decisions must be justified, recorded in minutes, and published on https://apply.uc.pt/ . VII.IX - Jury Composition: President: Isabel da Silva Henriques Effective
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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
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to provide the supporting documents by the grant contracting stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. 4. Work Plan The work