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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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. Furthermore, a novel predictive algorithm of School-age neuropsychological outcome will be developed combining radiomic model of brain development, with qualitative neonatal MRI findings. Achievement
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 hours ago
traditional predictive attempts and limits the availability of training data for high-resolution atmospheric and hydrological models. This limitation is compounded by the fact that many atmospheric reanalysis
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technology. Development of cutting edge foundation models for protein design, small molecule property prediction, or protein function prediction Data generation and curation, including molecular simulation and
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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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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 2 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
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: Textual Prediction of Survival (LLM classification & Attention Modelling) This project develops a model to predict patient survival by analyzing heterogeneous clinical documents. Unlike traditional methods
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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