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This project aims to identify novel methods for inferring actors, activities, and other elements from short message communications. Covert communications are a specialist domain for analysis in the Law Enforcement (LE) context. In this project we aim to improve law enforcement’s understanding of...
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on developing robotics and cyber physical systems solutions using machine learning and artificial intelligence to support different aspects of marine science, with opportunities to expand to other areas
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We invite applications for a postdoctoral researcher to join the UMLFF project at the University of Luxembourg. The project aims to develop the next generation of uncertainty-aware machine-learning
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challenges of our time. Be part of change As part of the interdisciplinary “Machine Learning Enhanced Sensor Systems” team, you will work on and develop data-driven solutions for intelligent sensor technology
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optimization modeling. An ideal candidate would use their foundational knowledge of various modeling methods to develop and apply cutting-edge machine learning and data analysis techniques for assessing and
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). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 16 hours ago
Position Information General Information Position Number POST40 Working Title Post-Doctoral Fellow-Machine Learning in Thermal-fluid Sciences Division Academic Affairs Department College
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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Bridging Causality and Forecasting in Space Weather: A Transparent Complex Systems Modelling and Interpretable Machine Learning Framework School of Electrical and Electronic Engineering PhD Research
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representations of time‑dependent data through sequences of iterated integrals and have recently gained significant attention in machine learning and data science. The project will investigate how