35 electrical-machine-"https:" "https:" "https:" "https:" "https:" research jobs in Portugal
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Programme under Portugal2030 and the European Union, operation code COMPETE2030-FEDER-02975500 (n.º 24829) taking place at the Department of Informatics of the University of Beira Interior(http
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Programme under Portugal2030 and the European Union, operation code COMPETE2030-FEDER-02975500 (n.º 24829) taking place at the Department of Informatics of the University of Beira Interior (http
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: Co-design of Context-aware Adaptive Mobile Authentication for Older Adults”, “2024.14974.PEX”, “DOI: https://doi.org/10.54499/2024.14974.PEX ”, funded by the Fundação para a Ciência e a Tecnologia, I.P
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Regulation for Research Studentships and Fellowships, in its current version (Reg. 950/2019 published in DR on 16th December: https://dre.pt/application/file/a/127230968 , or at the FCT website: https
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possible renewals, a cumulative period of four years of research fellowship intended for doctoral students. Preferential factors: Research experience in the areas of Bioinformatics and Machine Learning
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Holder statute, approved by Law no. 40/2004, dated August 18th, in its current wording; Regulamento nº 950/2019 [Regulamento de Bolsas de Investigação da FCT, I.P.] (https://www.fct.pt/apoios/bolsas
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
or ideological beliefs, or trade union membership. Where to apply Website https://inegi.pt/pt/ Requirements Research FieldEngineering » Computer engineeringEducation LevelPhD or equivalent Research
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive
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both Portuguese and English in the Apply UC computer platform and in the EURAXESS Portugal Portal, at https://www.euraxess.pt/ . Pursuant to paragraph h) of Article 9 of the Portuguese Constitution
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the numerical modelling of thermal phenomena, with particular emphasis on solidification processes. d) Knowledge on machine learning methods or data-driven modelling approaches applied to materials science or