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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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Contract: Full Time/Fixed Term for three (3) years.Supervisor:Dr Mohamed Lamine Faycal BELLAREDJ, Assistant ProfessorDepartment of electrical engineering, Universite de Moncton (Moncton campus)Email
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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diverse backgrounds (e.g., economics, engineering, computer science, information systems, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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sensing hardware into a high-throughput sorting line is a plus. Proficient in developing, training, and deploying AI-driven sensor-fusion pipelines, including preprocessing, deep-learning model
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular