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of mill and production operations. The scientific challenge will be to use the model and machine learning alongside live mill data (temperature, rolling loads etc) to reverse engineer the current
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directly with speaker communities to ensure the technology is genuinely useful to them. You will gain deep expertise in machine learning and natural language processing, access to high-performance computing
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, and machine-learning-based analytics. The research work at NTNU will focus particularly on automation, robotics, mechatronic design, sensor integration, and intelligent experimental systems required
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advanced machine learning techniques Optical design and miniaturization of a novel spectroscopic sensor based on freeform optics, optimized for agrifood applications For this function, our Brussels
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required. To learn more about these options, please contact the NRC Hiring team using the contact information below. Note: This is for three continuing positions and anticipatory staffing for position(s) of
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, a strong degree in computer science, cybersecurity, mathematics, or a related subject. Experience with cryptography, machine learning, or systems implementation is valuable, as are solid programming
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of machine learning / AI methods and their applications to experimental data; Knowledge of data science (statistics and quality control) as it relates to experimental data and ability to automate data storage
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of the following criteria: Technical competencies Knowledge of machine learning / AI methods and their applications to experimental data; Knowledge of data science (statistics and quality control) as it relates
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on the field can be derived from first principles, and how these constraints can improve the technique's performance, particularly when embedded in modern machine learning models. The ultimate goal is to
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machine learning venues (e.g., NeurIPS, ICLR, CVPR) and validate research on state-of-the-art edge computing testbeds. Project description For technical reasons, you must upload a project description