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available in Germany within the Marie Skłodowska-Curie Actions (MSCA) Doctoral Network TRILOGY, funded under Horizon Europe. The selected doctoral candidate will work on an interdisciplinary research project
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studies have demonstrated the limitations of current ground- and satellite-based observational networks. The PhD project aims for a new method to derive ground-level concentrations from satellite-based
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Join our scientific team in the multimedia communications group in a key position in
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Networks (PINNs), PyTorch, TensorFlow is advantageous Experience with Fortran subroutines, automation scripting for FE software and CAD design (Solidworks, Catia) is advantageous Good language skills in
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simulations for glass molding processes, including model setup, meshing, debugging, result extraction, and evaluation. You will also post-process simulation data in Python for optimization and machine-learning
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computing ecosystems Proven experience in developing and applying deep learning methods for image analysis, such as convolutional neural networks (CNNs) for segmentation and classification Practical
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of photonic microsystems, related technologies including nanoelectronics and wireless communication solutions, we create - in flexible and interdisciplinary teams - technologies for innovative products in a
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optical communication networks and systems as well as the coding of video signals and data processing. Become a part of our team and join us on our journey of research and innovation! Be part of change
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optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD position - "Smart preparation of air pollutant satellite retrievals for optimizing emission inventory data" within the HDS-LEE