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, wearable devices, and human movement. The ideal candidate will demonstrate experience conducting interdisciplinary research with healthy or clinical populations that applies AI models to predict relevant
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aging. The main task is to develop methods for predicting health outcomes using dynamic and adaptive modeling whilst addressing computational challenges the analysis pose. This will contribute
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with semi-analytical predictive models, to establish new physical principles for designing high-efficiency, low-noise multi-rotor configurations. You will have access to state-of-the-art facilities
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focuses on advancing data-driven and model-based methods for fault detection, predictive maintenance, and process monitoring. The successful candidate will conduct research in data-driven and model-based
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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exploratory analysis on large, multi-dimensional datasets; (b) develop predictive/diagnostic models and algorithms to lead and support clinical/translational research; (c) collaborate with cross-functional
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is a great opportunity to gain a deeper understanding of what it takes to process data and build and evaluate predictive models. This position will be full-time for approximately 37.5 hours per week
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methods to understand and predict the adsorption, self-assembly, and protective behavior of N-heterocyclic carbenes (NHCs) on metallic and oxidized surfaces. NHCs are promising corrosion-inhibiting
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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize