66 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at University of Aveiro
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Aveiro - Civil Engineering DepartmentCountryPortugalCityAveiroPostal Code3830-193StreetCampus Universitário de SantiagoGeofield Contact State/Province Aveiro City Aveiro Website https://www.ua.pt/pt
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Website https://www.ua.pt/pt/decivil Street Campus Universitário de Santiago Postal Code 3830-193 E-Mail romvic@ua.pt STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E
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of the assessment described under Section 5 below. 2.3 — The compulsory application minute, to be completely filled out, dated, and signed, is available at https://www.ua.pt/file/78384 . 2.4 — Submission
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Degree or equivalent Additional Information Website for additional job details https://www.ciceco.ua.pt/files/edital_pt_revamp.pdf Work Location(s) Number of offers available1Company/InstituteUniversity
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to the sub-items of the assessment described under Section 5 below. 2.3 — The compulsory application minute, to be completely filled out, dated, and signed, is available at https://www.ua.pt/file/78384 . 2.4
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of the assessment described under Section 5 below. 2.3 — The compulsory application minute, to be completely filled out, dated, and signed, is available at https://www.ua.pt/file/78384 . 2.4 — Submission
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of the assessment described under Section 5 below. 2.3 — The compulsory application minute, to be completely filled out, dated, and signed, is available at https://www.ua.pt/file/78384 . 2.4 — Submission
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of the assessment described under Section 5 below. 2.3 — The compulsory application minute, to be completely filled out, dated, and signed, is available at https://www.ua.pt/file/78384 . 2.4 — Submission
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at: bolseiros – sgrh – Universidade de Aveiro (ua.pt). ¹ Note: Confirm the type of financial support of the Project at [https://financeiros.ua.pt ](https://financeiros.ua.pt ) in the funding acceptance term
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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