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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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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
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