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Details The aim of this project is to combine nanomechanical methods with modelling (i) to develop quantitative, predictive models for the behaviour of molecules in sliding contacts, and (ii) to understand
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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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Application deadline: All year round Research theme: Environmental geochemistry How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD studentship is open to EU, UK, and US applicants. The
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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methodologies, advanced controller synthesis, performance and stability assessment. Trade-off, prototyping and selection of advanced DFAOCS control methodologies: minimum set to be explored: model predictive
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . The research associate will focus on Vision-Language Model based situation awareness and decision-making
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 19 hours ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as