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(e.g., FactSage, Thermochimica, Thermo-Calc) for multicomponent molten salt systems. Knowledge of machine-learning potentials applied to molten salt simulations is an advantage. Postdoc 3 In this project
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, modelling and machine learning to improve defect detection, classification and power loss simulations. Benchmarking field-acquired images with laboratory measurements. Publishing results in leading journals
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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Requirements Applicants must hold a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, or a closely related field. A strong research background and programming experience
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, bioinformatics, aging biology, epidimological data and AI-driven systems modeling. The successful candidate will develop and apply computational and machine learning approaches to decode the molecular and
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. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and process-based radiative transfer models
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Graph Machine Learning and Graph Data Management At Section for DATA, Department of Computer Science, Aalborg University, a postdoc position is available. The project is funded by a Novo Nordisk
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. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and process-based radiative transfer models
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, unlocking reliable perception and navigation where GNSS/GPS cannot be trusted or is unavailable. The project combines ultrasonic sensing, probabilistic perception, and machine learning with advanced robotics
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Graph Machine Learning and Graph Data Management At Section