87 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Universidade de Coimbra in Portugal
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' performance will be assessed according to the following weights and criteria: - Criterion 1 - Knowledge in the areas of Bioinformatics, Artificial Intelligence and Machine Learning - Criterion 2 – Motivation
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engineering Engineering » Mechanical engineering Engineering » Other Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions Application Deadline 8 Apr 2026 - 23:59 (Europe/Lisbon) Country
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engineering Engineering » Other Engineering » Mechanical engineering Engineering » Chemical engineering Engineering » Industrial engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor
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available at http://www.fct.pt/logotipos/ and, when applicable, the logos of the European Union and the Operational Program, following the graphic standards available on the websites of the respective
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- Work Plan / Goals to be achieved: In the first phase, samples will be produced using the WAAM process, which will subsequently be machined to obtain specimens with appropriate dimensions and geometries
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: 857524), and the Operational Program of the Centro Region of Portugal (CCDRC Ref: CENTRO-45-2020-75), in the conditions described below. The full text of the Call is available at https://www.uc.pt/mia
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) Based on the score of their academic qualifications. VII.VIII -All provisional and final jury decisions must be justified, recorded in minutes, and published on https://apply.uc.pt/ IT137-26-145. VII.IX
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necessary for the service to be provided outside the usual workplace. Where to apply Website https://app-4.apply.uc.pt Requirements Research FieldOtherEducation LevelPhD or equivalent Additional Information
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- The curriculum evaluation considers the candidates' academic and professional achievements based on the following weights and criteria: - Criterion 1 - Academic performance in subjects within the areas of Machine
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objectives: 1 – Development of a tool for identifying operating regimes using machine learning techniques. 2 – Development of a tool for identifying the causes of process eco-efficiency degradation using