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Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
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from renewable electricity and sustainable raw materials, represent a promising solution, enabling deep decarbonization. DESIRE is a Marie Sklodowska-Curie doctoral network that aims to train 15 doctoral
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investigate deep learning methods for local data augmentation and adaptive point density control, addressing the anisotropy and uneven sampling typical of urban LiDAR. You will work on a four-year doctoral
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experience with deep learning, machine learning and/or time series analysis. Good programming skills in Python or similar languages. Experience with using machine learning in the context of neuroscience
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of the bioprinting process. Objective 2: Training of a deep learning model to predict inputs that will achieve bioprinted scaffolds with the required print fidelity and scaffold micro-architecture. Objective 3
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learns auditory information. Methodologically, the project will explore the integration of information-theoretic decompositions, deep neural architectures, and large language models (LLMs) as powerful
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experience in common deep learning frameworks (e.g., PyTorch and TensorFlow) would be a benefit; The qualities to carry out independent research, demonstrated e.g., by the grades obtained in your (under
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technology and/or computer graphics as well as interest in fundamental research and experimental working. Strong skills in VR, psychophysics, deep learning or computer simulation are another advantage Job
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learning, non-Hermitian systems The Quantum AI lab at ETH (Prof. Juan Carrasquilla ) invites applications for PhD positions to work at the intersection of computational quantum many-body physics, machine