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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | about 2 hours ago
a deep understanding of state-of-the-art numerical solution methods, including finite elements, finite differences, geometric multigrid methods, and their parallelisation architectures and challenges
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the problem is explicitly considered. In particular, it will investigate how to tightly integrate state-of-the-art sampling-based methods with state-of-the-art methods from numerical optimal control in a
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of numerical precision/instabilities. Different physical configurations of NEMO, either local or global, will be studied. We also plan to optimize the threshold values in NEMO, taking into account the various
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processes for data standardization, data warehousing, quality assurance development and data analysis. A successful candidate will work closely in different aspects of the quality assurance and data
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different optimization methods using low rank tensor minimization and tensor decompositions paired with auxiliary information in order to recover missing links in a multilayer network with connected
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experience developing biological application an additional plus Experience coding / applying finite difference, finite element or finite volume methods Experience using optimization software such as GAMS
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. We leverage advanced technologies like semantic data processing, signal processing, and network resource management to enhance performance. To optimize and analyze complex 6G networks, we use AI/ML
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passionate about hydrology, environmental modelling, and working across disciplines, this position offers a unique opportunity to make a real-world difference! Expected start date and duration of employment
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possible until 31 December 2027. Key responsibilities and duties: Use analytical and numerical mathematical tools to design a sensor system using infrasound and ultrasound. Propose different sensor systems
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Systems will participate in the research efforts of developing systems integration, analysis, design, control, and/or optimization models and algorithms for smart energy systems to enable smart and healthy