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management, and machine learning approaches for process monitoring and control For this function, our Brussels Humanities, Sciences & Engineering Campus (Elsene) will serve as your home base.
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Environment - Wiley Online Library Additive Manufacturing: A Comprehensive Review Big data, machine learning, and digital twin assisted additive manufacturing: A review - ScienceDirect Full article: Achieving
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of machine learning algorithms are of real interest in improving the accuracy of water quality measurements, particularly in identifying, accounting for, and neutralizing ionic interference. The second key
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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and Space Weather. The successful candidate will contribute to the development, testing, and operation of solar monitoring stations, real-time data pipelines, and AI-based analysis tools. The position
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preprocessing IoMT network traffic datasets. Implement and evaluate machine learning algorithms (e.g., logistic regression, SVM, random forest) for intrusion detection. Develop prototype software tools (e.g
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the application of machine learning and artificial intelligence. By using neural networks developed in Python, the project aims to generate robust and generalisable models for scaffold design. Industrial
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configurations and illumination conditions. Implement and validate device-independent representations. Investigate and apply domain adaptation and transfer learning techniques to develop models that generalize
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» Electrical engineering Engineering » Other Researcher Profile First Stage Researcher (R1) Country Estonia Application Deadline 26 Oct 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Full-time Hours
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning