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Field
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PhD candidate will have: A strong background in computer science, artificial intelligence, machine learning, or a closely related field. A solid understanding of machine learning concepts, particularly
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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In this position, you will join our Simulation and Data Lab for AI and Machine Learning for Remote Sensing . The lab advances interdisciplinary research and operational services by combining
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planning and decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch
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individuals participating in studies investigating the cardiovascular consequences of preterm birth. By combining fluid dynamics, machine learning, and advanced imaging analysis, the project seeks to develop
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integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
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Digital manufacturing, Industry 4.0, or cyber-physical systems o Product design for disassembly, remanufacturing, or recycling o Data analysis, AI, or machine learning applied to engineering systems
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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 14 hours ago
Do you want to help transform human health through machine learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental
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microscopy and SEM, with computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties