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of this project is to create a radiomics and radiogenomics based approach to describe and create predictive models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK
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courses of Higher Education Institutions. Preference factors: • Experience with computational simulation models / MATLAB/Simulink.; • Knowledge of industrial-grade communication protocols (Modbus TCP, IEC
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-of-the-art deep neural networks for musical audio, with special focus on timbre analysis and manipulation.; - Identify and implement approaches for explainable ML models.; - Cooperate in writing scientific
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-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: In this project, we intend to develop a new approach based on physically inspired hybrid machine
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generative models for the creation of human movement datasets for training AI models; - Prepare activity reports and scientific articles. 4. REQUIRED PROFILE: Admission requirements: - Master's student, with a
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awarded to students who are enrolled in non-award courses, or up to four years, in the cases of students enrolled in a PhD. Scientific advisor: Tânia Esteves Workplace: INESC TEC, Braga , Portugal
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awarded to students who are enrolled in non-award courses, or up to four years, in the cases of students enrolled in a PhD. Scientific advisor: João Tiago Paulo Workplace: INESC TEC, Braga, Portugal
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architectures for the energy and power management system on ships Develop control and dispatch strategies for hybrid microgrids, taking into account the specific power and energy requirements of ships Modeling
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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: The overall vision of the ATE is to deploy and demonstrate a set of business models
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Institutions. Preference factors: • Experience with computational simulation models / MATLAB/Simulink.; • Knowledge of industrial-grade communication protocols (Modbus TCP, IEC 61850, etc.). ; Minimum