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detected at a regional scale. The implementation of advanced InSAR processing chains will provide new insights into the phenomena observed and enrich the databases required for deep learning methods
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progression modelling, exploiting advances in deep feature learning and uncertainty quantification to support the Bayesian framework, as well as implementation of computational models of neurodegeneration
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and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow or Pytorch is advantageous Experience in numerical methods for partial
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. PhD Project Description Project Title: Photonic-Inspired Federated Learning for Communication-Efficient Next Generation Radio Access Networks (NG-RANs) Description: This PhD project will develop a novel
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, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap camera footage for wildlife monitoring and conservation efforts. Job Responsibilities
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impact the safety of flight. The thesis shall develop robust state estimation methods by combining factor graph-based sensor fusion, variance component analysis, and modern deep learning approaches such as
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Deep Learning libraries (e.g., Pytorch, Tensorflow, Keras) will be considered a significant advantage; Previous experience in image processing and\or computer vision will be considered an advantage
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expertise in the RTG-addressed PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, very good verbal and written English communication skills as well as the absolute
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control framework for microfluidic live-cell analytics in close collaboration with partners at HZI, Helmholtz Munich and HHU. Your tasks in detail: Establish deep-learning–based segmentation, species
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future. Fuelled by curiosity and a deep sense of responsibility, they provide invaluable contributions to research and teaching, thus enriching our society. Are you also inspired and driven by the desire