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behavior of these components will be developed based on Finite Element Methods (FEM) complemented by Machine Learning models. Legislation and Regulations: Statute of Scientific Research Fellow, approved by
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(pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis
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tasks in which the candidate was involved). Proven knowledge and experience in the use of qualitative and quantitative research methodologies. Experience in using computer tools, namely SPSS and NVivo
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and signature matching based on existing code. - Performance evaluation of the application on a Raspberry Pi (RPi). - Development of improvements to machine learning algorithms for anomaly detection and
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to translational R&D project coordination at the interface between biotech and academia. Visit the link to access full mandatory requirements: https://drive.google.com/file/d/1XDXQtA4qDGWvBw1YR5lQt2APng3oCE7y/view
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for this grant: Requirement 1: - Be a student enrolled in a doctoral program in the area of Materials science, Machine Learning computational science, Coating and surface engineering a requirement to be duly
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attending an academic Bachelor’s degree in the scientific field mentioned above. Knowledge or experience (preferred) on machine learning or computer vision techniques, and interest in developing such skills
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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include
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a strong background in advanced computing and data science, machine learning, or in a related field, with expertise in monitoring data reliability, quality assurance, and AI modelling techniques
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laser repair system that integrates corrosion assessment, cleaning, cutting, repair, and painting within a single robotic unit. Using computer vision, machine learning, and predictive models, it enables