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for 1 research grant within the framework of project “Understanding Machine Learning Systems”, financed by Faculdade de Engenharia da Universidade do Porto, under the following conditions: Scientific Area
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in the project proposal for Profile 7, in particular: Task3: Multimodal Data Analysis and Machine Learning; Task4: Coating Optimization and task: Dissemination. The work will focus on the study and
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. Selection procedure: The curriculum of the candidates (60%) will be evaluated based on the merit of the candidate and the following factors will be weighted: a) Academic training (Master in Mechanical
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for this grant: Requirement 1: - Be a student enrolled in a doctoral program in the area of Materials science, Machine Learning computational science, Coating and surface engineering a requirement to be duly
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. Define a climate shelter system for a municipality, based on the methodology developed. 4. Assess and validate the shelter system. 5. Develop an e-book to compile the main results of the ClimSHELL
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2022.09373.PTDC financed by national funds through FCT/MECI, under the following conditions: Scientific Area: Machine Learning/Recommender Systems Admission requirements: Candidates who cumulatively meet the
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will be part of the project team of Agenda GreenAuto: Green Innovation for the Automotive Industry, no. C644867037-00000013, investment project no. 54, in the context of PPS10: Neural Network-based
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an innovative solution based on hydrocyclones for the removal of microplastics from urban wastewater as a quaternary treatment. Simulation skills will be considered an asset. Legislation and regulations: Law Nº
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of the candidates (60%) will be evaluated based on the merit of the candidate and the following factors will be weighted: a) Academic training (Master in Chemical or Biological Engineering or Bioengineering 10 points
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: The curriculum of the candidates will be evaluated based on the merit of the candidate: Academic training: Master’s in chemical engineering – 5 points; other master’s – 2 points. Master’s final grade: equal