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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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optimization of laser deposition coating processes using combined wire and powder feed, including numerical simulation of laser modelling, and process parameter optimization. Legislation and regulations: Law Nº
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models
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to reduce the amount of required training data while maintaining high predictive accuracy. Methods and Techniques : Density Functional Theory, Machine Learning for atomistic modeling Location : Institut Jean
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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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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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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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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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workflows, and data engineering for mobility platforms • AI/ML for transportation prediction, system optimization, and environmental/health impact modeling • Deployment of decision-support tools for public