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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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with e-CALLISTO instruments or Software-Defined Radios (SDRs). · Familiarity with machine learning for astrophysical data analysis. · Knowledge of solar radio data pipelines and event classification
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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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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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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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optional skills and qualifications: Previous research experience, particularly in the fields of Internet of Things security and machine learning model security applied to intrusion detection. Contracting
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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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(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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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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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