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-award courses of Higher Education Institutions. Preference factors: - programming experience in Python; - knowledge of computational learning and computer vision Minimum requirements: - degree average
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skills;, Fluency in English and Portuguese (spoken and written). Preference factors: Experience in Machine learning, network sciences, Explainability, and network science;; Use and programming languages
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new approach based on physically inspired hybrid machine learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION
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TEC. 2. OBJECTIVES: Study and explore machine learning techniques for knowledge extraction and anomalies from data generated by IoT sensors in agroforestry and pastoralism 3. BRIEF PRESENTATION
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TRAINING: - survey the state of the art in emerging wireless networks, including their simulation using real data assimilation, machine learning, and digital twin approaches; - collaborate in the writing
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-award courses of Higher Education Institutions. Preference factors: - Knowledge about machine learning algorithms and/or operations research.; - Knowledge about electric power systems.; - Experience in
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background) with supervised and reinforcement learning - Academic background in operations research - Programming knowledge in Python. Research FieldEngineering » Computer engineeringYears of Research
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, supported by INESC TEC. 2. OBJECTIVES: The objectives for this grant are as follows:; • Research and develop machine learning algorithms for the processing of gastric endoscopy images.; • Lead or support the
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' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: Experience in research projects. Minimum requirements: Experience in Computer Vision and machine learning. 5
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PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in