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well as independently. The successful candidate will be involved in two activities involving synthesis, electrode design and advanced characterization of the materials for Li-ion and Na-ion bat-teries under two projects
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. Experience with laboratory experiments (batch incubations, microcosms, or similar). Knowledge of chemical analysis techniques, such as ICP-OES or ICP-MS, ion chromatography, and nutrient analysis. Experience
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heterogeneous sources of information—including remote sensing, forest inventory data, existing forest maps, environmental and climatic data, and emerging large-scale AI representations—can be jointly exploited
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paid to performance across datasets, content sources, generation methods, and real-world transformations such as compression, resizing, and re-encoding. The final scientific scope will be refined
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predictive maintenance of ships and maritime systems. Modern vessels generate large amounts of heterogeneous operational data from sensors, machinery, control systems, maintenance records, and other sources
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, including industrial applications in geology. Your immediate leader will be the Head of Department. Duties of the position Simulate MRI data and model disturbances arising from various physical sources
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of the position Simulate MRI data and model disturbances arising from various physical sources Develop, implement and train INR models for MRI time-series data Analyze functional MRI data of the spinal cord Present
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information about the project, funding sources, partners and/or a link to the project's website. Duties of the position Complete the doctoral education until obtaining a doctorate Carry out research of good
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outside academia. Your immediate leader will be the head of the Computing Unit. About the project Deleted if not applicable. Here you can enter brief information about the project, funding sources, partners
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
tactical sourcing decisions, and contribute to improved supply chain performance. You will join the Production Management Research Group at NTNU, which focuses on the design and planning of production and