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of virtual models (digital twins) for studying and detecting abnormal operating conditions (predictive diagnostics of anomalous behaviors) in distribution transformers using the finite element method (FEM
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artificial intelligence-based algorithms to optimise operation and predict anomalies in water distribution networks. The algorithms developed should identify patterns and anomalies that indicate the presence
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. Interest in clinical algorithm development and dexterity with biostatistical coding in R or Python is a plus. The primary goal of this aspect of the CH CARE Study is to combine serially obtained somatic and
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existing tools and databases into high-throughput pipelines, and facilitate the display and the distribution of processed data. Related projects and responsibilities will include: Statistical analyses
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for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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borehole electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed
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leveraging on AI and optimization, applying data science and analytics techniques. Such tools will support the integration of Distributed Energy Storage (DES) and Distributed Energy Resources (DER) in
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, deliberative workshops with potential pilot communities, surveys, and comparative policy review to capture needs and equity concerns, build an operational model of BC’s transmission system, design distributed