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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
, the shortlisted candidates will be invited to an interview. The final evaluation will be based on: Curriculum Assessment: 40% Interview: 60% Selection criteria include scientific merit, qualifications, alignment
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and analyze relevant failure modes and degradation mechanisms, using methodologies such as FMEA and RCM, ensuring their alignment with decision-making problems in asset management.; - Develop models
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Evaluation – 20%, analyzing the applicant’s motivation, the alignment of their academic background with the proposed project, and the coherence of the expectations presented; c) Interview – 20%, aimed
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reference: IT160-26-34 Within the framework of project "Releaf – Next-generation Sustainable Prebiotics for Precision Immunonutrition: Applications in Multiple Sclerosis", supported by the European Regional
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cells and the collinear alignment of the pump beam. In parallel, they will participate in the development of a long-range LiDAR test platform, with a focus on experimentally investigating how speckle size
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evolutionary methods will be employed to track karyotype evolution across multiple generations to unveil the evolutionary fate of chromosomes in aneuploid and cell-size mutants. Legislation and Applicable
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to the Portuguese grading scale, by the application deadline, the applicant will receive a score of 1 point. Alignment of the scientific curriculum vitae with preferential requirement b (0 to 10 points): i. Very
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or cooperation between a High Education Institution and one or multiple research units. Work plan: a) Design, development, and assembly of an experimental system for the characterization of perovskite photovoltaic
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
; Collaboration within multidisciplinary teams and across multiple workstreams simultaneously; Production of scientific publications, scientific software, computational tools, technical reports, and contributions
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acquired across multiple anatomical regions to form a patient-level assessment. This PhD project will investigate novel deep learning methodologies that jointly model anatomical structure and prediction