50 machine-learning-"https:" "https:" "https:" "https:" Fellowship positions in Portugal
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of article nº 9 of the Scientific Research Grant Regulations of the University of Aveiro. 5. Work Plan: Intelligent, modular battery with machine learning algorithms. The aim is to develop a high-performance
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for developing machine learning models for the automatic identification of species from images collected through electronic monitoring systems (Work Package 3 – Bycatch Monitoring). The candidate will be involved
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of programming and artificial intelligence.; - Knowledge of deep learning and computer vision.; - Autonomy. Minimum requirements: Strong knowledge of the English language (written and spoken). 5. EVALUATION
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the field of the seismic behaviour of masonry structures and machine learning; Have a good proficiency of the English language. At the time of the respective hiring, candidates must prove that
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, and eye tracker data. Work Plan: - Multimodal feature extraction from EEG, HRV, gaze dynamics, and pupil size data; - Signal fusion and model training using interpretable machine learning models (e.g
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Requirements: Applicants must be enrolled in a Master’s Degree in Computer Engineering or related fields. Proof of enrolment must be provided by the time of contracting. However, candidates may initially submit
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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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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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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