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, segmentation, and metric-semantic mapping - Develop and evaluate machine learning-based image segmentation methods for robust perception in extreme environments; - Contribute to metric-semantic mapping
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Web of Science in the required fields of knowledge; c) Have experience in the development of optimization models and methods, as well as their implementation and testing. 5. Formalization
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component: - Undertake advanced training in qualitative, quantitative, participatory and geospatial research methods. - Participate in seminars, scientific meetings and training activities organised by
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[LM5.1] VII - Selection Methods: Evaluation of the academic record (AR) - 40% and Interview (I) – 60% VII.I - AR - In the evaluation of the academic record, the candidates' academic performance will be
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of plant-based ingredient formulations with improved sensory profiles and added functional properties. 2. Applicable Legislation Decree-Law no. 57/2016 of August 29th, amended by Law 57/2017 and Regulatory
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; Photocopy of the qualification certificate of the academic degree required - Master's degree in Mathematics, Applied Mathematics, or related areas, with a strong mathematical component degree; If the academic
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be of the handle type (e.g. DOI) or URN (e.g. ISBN)). III.2.5. Any other elements that the candidate considers relevant. III.3. All application documents indicated in item III.2. shall be submitted in
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
computational modelling and simulation in engineering, including numerical methods, finite element analysis, constitutive models, response prediction, or digital twins (0–4 points); Experience in computational
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monthly by bank transfer. Eligibility criteria Selection criteria: The selection methods to be used and the respective weighting will be as follows: a. Curricular assessment (grade from 1 to 20) weighting
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Vitae (or indication in the CV of the unique digital identifier of the publications, which must be of the handle type (e.g. DOI) or URN (e.g. ISBN)). III.2.5. Any other elements that the candidate