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. Combining AI-based prediction (e.g., TCNN, LSTM, etc) with musculoskeletal models to estimate and predict muscle activation and tendon force over short horizons (e.g. ~200 ms). Integrating these predictions
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materials science by integrating physics-based simulations with data-driven analysis of cutting-edge synchrotron radiation facility data. By combining experimental data with physical models, we establish
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observations with hydraulic models and digital twins, new predictive tools can be developed to identify increasing failure risks and support proactive monitoring and maintenance strategies for drinking water
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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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light–matter interaction through appropriate transport models, properly accounting for attenuation effects due to the materials. The activities will be carried out within the EIC PATHFINDER PREDICT
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of the contact line, which is still only partially understood and predicted. The present thesis proposes to develop an original experimental approach based on the simultaneous coupling of several optical
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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
clinical application. The focus is particularly on photon-counting computed tomography (PCCT), 4D MRI flow imaging, and AI-supported analysis and modeling methods (e.g., CT-FFR, predictive software models
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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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computing. This particular position focuses on time-series analysis and forecasting using transformer based foundation models. About the Project Time-series prediction using transformer based models is
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challenges of learning from network traffic, (ii) train original AI models that are designed to operate precisely on such data, and (iii) demonstrate the viability in production of AI-driven solutions for, e.g