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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied
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Your Job: You will be a member of a consortium of leading research institutes and an industry partner. Your task is the build-up of a predictive model for tandem cell stability. Your tasks in detail
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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead
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modeling will be applied to quantify actin and myosin flows on curved cellular surfaces, capturing directionality, stability, and fluctuations associated with pole formation. Finally, image-derived features
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friendly mode of transportation, but noise and vibration remain a major environmental challenge for the railway sector. Therefore, a wide range of numerical models have been developed for the prediction
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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believe that generative pre-training offers a promising path to a new class of models that work across settings and can support prediction of many different clinical outcomes at once. To fuel your models
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to advance predictive modeling frameworks for soft, rate-dependent materials under complex thermo-mechanical loading conditions. The research will involve close integration of analytical constitutive modeling
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& Budget Strategy Institutional Analytics & Decision Support Policy, Planning & State Operations University Business Services The Modeler supports the University’s strategic, financial, and operational
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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