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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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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution
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Centre for Advanced Robotics Technology Innovation (CARTIN) is looking for a candidate to join them as a Research Fellow. Key Responsibilities: Develop novel algorithms for multi-agent inverse
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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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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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-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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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing