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Field
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demonstrate suitable experience in computer science, machine learning, robotic vision, or a related field (through a high-quality Honours or Masters degree). The successful candidate must be able to enrol as a
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collection activities. Supervision will be provided by academics from various disciplines specializing in biomechanics, image processing, and computer vision, alongside orthopaedic surgeons and academics.
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multiphase flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid
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machine learning with the logical reasoning and semantic understanding of symbolic AI (often referred to as material and design informatics) is being developed for the accelerated discovery and development
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collaboration with the Materials Design Division (also at IFM) and with the Computer Vision Laboratory at the Department of Electrical Engineering (ISY), a world-class research environment specializing in
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unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position The Industrial Ecology Program (IndEcol), Department of Energy and Process
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities
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consider novel design principles combining approaches in biosensors, communication systems, and machine learning. Are you motivated to take a step towards a doctorate and open up exciting career
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Compression of quantum data under unreliable entanglement assistance Joint compression and error correction for robust communication in the quantum-classical internet Quantum embeddings for machine learning
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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between