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- MOHAMMED VI POLYTECHNIC UNIVERSITY
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with traffic jams spreading like oil spills over entire networks. We believe traffic management based on reliable predictions is therefore crucial to ensure accessibility and safety, especially during
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complex dynamic system. Minor disruptions can lead to major delays with traffic jams spreading like oil spills over entire networks. We believe traffic management based on reliable predictions is therefore
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neural network tool to design multilayered compound metasurfaces for multifunctional operation. • Collaborate with nanofabrication teams to prototype and validate designs. • Conduct optical
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are seeking a highly motivated researcher with a strong background and interest in machine learning and artificial intelligence. The position focuses on the development and application of advanced deep learning
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, a proven publication record, and effective interpersonal skills. Preferred Qualifications: Knowledge of graph neural networks and other geometric deep learning approaches for graph-structured
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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Requirements: The prospective candidate should be well-versed with deep neural networks, have experience working on PyTorch or similar DL frameworks, programming in Python (preferred), NLP packages and pipelines
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relevant technical software, such as Cadence, SPICE, Verilog, etc. are needed; • Background knowledge in neural network algorithms preferred, but not required • Collaborative skills, student mentorship
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methods with optimization and decision-support models. Background in one or more of the following: time-series analysis, neural networks, forecasting, uncertainty quantification, sensitivity analysis
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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian