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Machine Learning model will be developed, capable of adjusting the electric assistance to optimise the balance between performance and consumption. Finally, the system will be validated with a real e-bike
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of establishing relationships between signal sources and predicting commands; 6. Design of machine learning and adaptive models that ensure the continuous evolution of the system, increasing the autonomy and
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that combine machine learning and classical methods. Work Plan: -State-of art revier and publication of a review paper -Development of classical approaches -Development of hybrid approaches -Journal publication
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experience in the fields of HRI, robotics, computer vision, or machine learning. Programming skills. Contracting requirements: Presentation of the academic qualifications and/or diplomas, if applicable
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under cyclic loading conditions, establishing robust numerical models for performance assessment in transport infrastructure applications, integrating Machine Learning technics in the process. The planned
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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RE-C05-i08 do Programa de Recuperação e Resiliência, através da Fundação para a Ciência e a Tecnologia - FCT, nas seguintes condições: Scientific Area: Computer Engineering, Biomedical Engineering
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to facilitate the integration of the framework with external systems and educational platforms; Establish a Machine Learning Operations (MLOps) pipeline to automate the lifecycle of models, including training
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-C05-i08 of the Recovery and Resilience Program, through the Foundation for Science and Technology - FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering
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-C05-i08 of the Recovery and Resilience Program, through the Foundation for Science and Technology - FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering