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of data analytics and mathematical modeling to predict clinically relevant biological outcomes using in vitro engineered tissue systems and in vivo models and will play a central role in the development
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teams. Unit URL https://imci.uidaho.edu/ Position Qualifications Required Experience Experience with statistical or predictive modeling as demonstrated by publications in the field Evidence of strong
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SyMulDaM project involving the development of predictive models to quantify the integrity and durability of a nuclear power plant containment structure., within the mechanical engineering department
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, and c) predicting new phenomena and discovering improved materials for applications. My efforts in this area use a variety of modeling approaches to answer questions on materials systems of interest
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 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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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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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Villenave d Ornon, Aquitaine | France | 10 days ago
be to develop predictive models dedicated to the ecological transition of agriculture. The project extends the understanding of links between metabolism programming and trade-offs with plant
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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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computational physics and/or geosciences and a strong interest in machine learning, Earth observation, and numerical modelling for Arctic prediction. The PhD project will develop methods to improve monitoring and