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learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework Simulation and analysis of batch and gradient elution
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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an impact in a societally relevant application In your application, please include a statement of research interest, CV, copies of exams, degrees and grades (transcript of records), a copy of your Master
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
is also leading the development and validation of novel machine learning methods for LEGEND simulations and analysis. We have been heavily involved in constructing, commissioning, and operating
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for a highly motivated PhD candidate to join our world-leading research program in Earth System modelling and improving Earth System Modeling by better merging of measurement data and model simulations
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numerical simulations to reproduce and predict magnetically confined fusion plasma experiments 2.Development of transport models based on simulation data and their implementation into integrated transport
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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the behavior of complex rotating machinery operating under extreme conditions. Through the integration of modeling, simulation, and experimental validation, the group supports the design, performance
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the project SOMIPs - Simulation, Optimization and Monitoring using AI-based Digital Twin Technology for Incineration Plants The call is governed by this Notice of Opening, the University of Coimbra Research