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(spoken and written), academic excellence, autonomy, curiosity, and attention to detail. Resumes demonstrating knowledge of programming in Python and/or Matlab; computer vision, image processing, learning
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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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from academic degree recognition processes. Preferential factors: a. Knowledge of developing artificial intelligence/machine learning (AI/ML) models and classifiers suited for embedded systems
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to predicting high utilization of healthcare services, using machine learning techniques applied to clinical data. The plan is focused on learning and skill development, with potential to contribute
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(spoken and written), academic excellence, autonomy, curiosity, and attention to detail. Resumes demonstrating knowledge of programming in Python and/or Matlab; computer vision, image processing, learning
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large sample size; d. Experience in integration of multi-omics using machine learning approaches; e. Experience in participation of research teams or projects. Candidates must be enrolled in a doctorate
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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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validation of machine learning algorithms for container transport planning problems, using real-time data from the tracking system. Taking a logistics perspective, the main objective is to consider the main
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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