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the evaluation of the academic record, the candidates' academic performance will be assessed according to the following weights and criteria: - Absolute merit of curriculum vitae (35%); - Knowledge of foreign
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criteria: - Criterion 1 – Absolute merit of curriculum vitae (60%); - Criterion 2 - Knowledge in the scientific areas of Design and Multimedia, and Computer Science and Engineering (20%); - Criterion 3
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weights and criteria: - Criterion 1 – Absolute merit of Curriculum Vitae (30%) - Criterion 2 – Masters average (20%) - Criterion 3 – Specific knowledge related to the work plan (15%) - Criterion 4
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to the following weights and criteria: - Criterion 1 – Absolute merit of curriculum vitae - Criterion 2 – Specific knowledge related to the work plan: Knowledge of neurodegenerative diseases is valued, with
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to the following weights and criteria: - Criterion 1 – Maters average – 30% - Criterion 2 – Especific Knowledge related to the work plan – 25% - Critério 3 – Publications in scientific journals and minutes – 10
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- Criterion 2 - - Specific knowledge related to the work plan: Knowledge of basic laboratory techniques and familiarity with computer programs for document processing, data analysis or image processing
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' academic performance will be assessed according to the following weights and criteria: - Criterion 1: Quality of the Curriculum - Proven knowledge of imaging data analysis software (10%), Proven programming
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the brightest minds to find creative solutions. Most importantly, you’ll discover what’s possible within you as you grow, make valuable contributions and progress in your professional journey. From ground
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic
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according to the following weights and criteria: - Criterion 1 – Absolut merit of curriculum vitae (50%) - Criterion 2 - Academic background (25%) - Criterion 3 – Specific knowledge related to the work plan