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platform. Where to apply Website https://fenix.tecnico.ulisboa.pt/fenixedu-admissions Requirements Research FieldEngineering » Computer engineeringEducation LevelUndergraduate Skills/Qualifications Admission
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on the development of methodologies and techniques of Evolutionary Computation and Machine Learning. V - Initial grant duration: 3 months V.I - Renewal Possibility: Possibily renewable VI - Funding and financial
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
the most significant microscopic parameters for the homogenized response Develop and train machine learning models to estimate the homogenized properties from such parameters Document research findings
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Computer Science, Computer Science and Engineering or related area. Additional optional skills and qualifications: Proven track record in ontology alignment, machine learning with biomedical data, and knowledge
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: familiarization with the workflow platform and machine learning concepts; development of web interfaces for data silo registration and federated training sessions monitoring; implementation of back-end components
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programming, and/or human-computer interaction (UX), and/or application of Machine Learning. Sense of responsibility and ability to communicate and integrate into multidisciplinary work teams. 3. Financial
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knowledge in areas related to Data Science; Have knowledge of Machine; Be a PhD Student in Information Management. Work plan and goals to achieve The work plan will include the development of Machine Learning
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' performance will be assessed according to the following weights and criteria: - Criterion 1 - Knowledge in the areas of Bioinformatics, Artificial Intelligence and Machine Learning - Criterion 2 – Motivation
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of Conception and Drawing of Mechanical Components and Systems, Product Development, Computer-Aided Drawing and Manufacturing, Computer Graphics, Image Processing and Analysis and Scientific Visualization. Work
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in mathematics and topics related to machine learning. EVALUATION CRITERIA The selection will be based on the following criteria: Academic CV (60%) Previous experience in related projects (20