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) to design an intelligent and scalable IoE energy management strategy; 4) to develop agile IoE fault detection and accurate failure prediction methods; 5) to construct an intelligent energy optimisation system
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learning workflows, and developing complete models. Example applications include drug design, cryo-electron microscopy, structural prediction and dynamic simulation of biological macromolecules, genomics
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in the Environmental Sensing and Modelling (ENVISION) unit
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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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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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-develop next-generation supply chain analytics leveraging predictive modeling, semantic models, AI, and natural language processing for advanced projects (e.g., semantic item classification, opportunity
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software for aerospace precision machining — you will develop physics-informed machine learning models that learn how individual machines actually behave, and use those models to drive a genuinely
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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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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in