58 machine-learning-"https:" "https:" "https:" "FCT NOVA UNIDEMI" positions at FEUP in Portugal
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-vision algorithms with edge-computing processing for the automatic detection of non-conformities. Machine-learning techniques will be applied to optimize cutting parameters, and the module will be
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23 Dec 2025 Job Information Organisation/Company FEUP Department Human Resources Division Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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the tender notice. The final decision on hiring is the responsibility of the top manager of the hiring entity. 14. Formalization of applications: 14.1. Applications must be formalized at http://www.fe.up.pt
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/10.54499/2023.11234.PEX , funded by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning applied in Applied to Fluid Dynamics Simulation Admission requirements
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. Formalization of applications: 14.1. Applications must be formalized at http://www.fe.up.pt/concursos , reference on-line nº 1413, until de 23h59m (local time) of 19 de january 2026. The application must include
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responsibility of the top manager of the hiring entity. 14. Formalization of applications: 14.1. Applications must be formalized at http://www.fe.up.pt/concursos , reference on-line nº 1400, until de 23h59m
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of applications: 14.1. Applications must be formalised at http://www.fe.up.pt/concursos , reference online no. 1416, until de 23h59m (local time) of 19/01/2026. The application must include: full name, number and
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manager of the hiring entity. 14. Formalization of applications: 14.1. Applications must be formalized at http://www.fe.up.pt/concursos , reference on-line nº 1419, until de 23h59m (local time) of 14/01
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at http://www.fe.up.pt/concursos , reference on-line nº 1422, until de 23h59m (local time) of 11/12/2025. The application must include: full name, number and date of identity card or citizen card, or civil
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improve existing degradation models. T3 - combine computer vision and machine learning models to identify, delimit and classify the facades’ anomalies in photographs and thermal images; integrate the new