140 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" Master positions in Portugal
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: Environment Engineering, or similar. Where to apply Website https://www.ipc.pt/bupc/concursos/form/concursos_bolseiros Requirements Research FieldEnvironmental science » OtherEducation LevelMaster Degree
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at: https://dre.pt/application/conteudo/127238533 ; and in accordance with the consolidated version with the changes resulting from the update, approved on December 10, 2025 by the Board of Directors
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- 072 Contacts: Phone +351 222061000; Email: geral@ess.ipp.pt ; Website: https://www.ess.ipp.pt/ DURATION AND WORK REGIME Scholarship contract for 12 months, eventually renewable until the duration
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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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of computational thinking and didactic knowledge in mathematics among future teachers of the Primary School; Prepare and submit scientific articles. Where to apply Website https://www.ipc.pt/bupc/concursos/form
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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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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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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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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