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contribute to developing new theoretical, historical, and practice-based insights on how heritage preservation and urban development interact in creating (or addressing) spatial inequalities in African and
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multidimensional networks by studying the theoretical foundations behind randomized algorithms in the context of sparse optimization and applications in real world data sets. We are also interested in exploring
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contribute to developing new theoretical, historical, and practice-based insights on how heritage preservation and urban development interact in creating (or addressing) spatial inequalities in African and
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are seeking a highly motivated candidate to contribute to the development of new catalytic systems through computational simulations and theoretical studies. Main Responsibilities: Perform computational
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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical
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-IoT system/network considering communication and data fusion requirements. Conduct a theoretical analysis of the developed designs. Develop simulations (writing code) to support the theoretical findings
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into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized algorithms in the context of sparse optimization and
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fields. Proven experience in developing deep learning models (e.g., CNNs, RNNs, LSTMs) for large-scale environmental data analysis. Familiarity with theoretical and practical aspects of scientific deep