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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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of Machine Learning (Theory or Practice). A successful candidate will be expected to lead a research team of graduate students as well as teach at the undergraduate and graduate levels. The position is open to
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Sciences (Theory or Practice). A successful candidate will be expected to lead a research team of graduate students as well as teach at the undergraduate and graduate levels. The position is open to
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for teamwork, problem-solving, and collaborative relationships Strong technical background in at least one of the following areas: Mathematical optimization, Resource allocation, Machine learning, Wireless
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sciences, or related fields Strong background in hydrology and remote sensing techniques. Proficiency in computer programming tools such as Matlab and/or Python. Knowledge of statistics and mathematical
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Cluster Contact: Pr. Johan Jacquemin – johan.jacquemin@um6p.ma Research Activities Develop independent research programs bridging experimental and Density Functional Theory (DFT) simulation of materials
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behavior within the storage system to optimize design and performance. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational
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advanced characterization methods of inorganic materials and their assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics
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assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation