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. The successful candidate will work on implementing a hybrid modeling scheme driven by remote sensing data to enhance the estimation of some of the significant water balance components. The candidate is expected
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the estimation of some of the significant water balance components. The candidate is expected to seek an optimal integration between the physical representations of the various processes and the computing power
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concepts and profitability. In-depth knowledge of environmental sustainability standards. Experience with data prediction and classification techniques. Computer Skills Good proficiency with optimization
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Osmosis (FO) — Reverse Osmosis (RO) system. The work of this project includes lab work, computer modelling, life cycle assessment, and techno-economic study. The project will contribute to protecting water
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parallelism in tensor operations. Collaboration: Collaborate with interdisciplinary teams including computer scientists, statisticians, and domain experts to apply tensor completion techniques to real-world
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efficiency. Investigate parallel algorithms and architectures that can exploit the inherent parallelism in tensor operations. Collaboration: Collaborate with interdisciplinary teams including computer