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Field
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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with exceptional precision and in unexplored energy regimes. We use a crossed molecular beam machine with a Zeeman decelerator, which enables precise control over the velocities and quantum states of
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of machine learning frameworks Your research will include using models and codes to investigate the optimized design, integration, and intelligent operation of thermal energy storage systems in industrial
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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advanced computational analysis of large-scale neural recordings. What you would be doing: Process and analyse large-scale calcium imaging datasets from multisensory experiments, including neural responses
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms