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Field
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? Join us to develop deep learning techniques for fusing acoustic sensor data with other vehicle sensors for robust multi-modal environment perception. Help shape the future of autonomous driving! Job
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in challenging deep learning at its core? And
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Are you interested in challenging deep learning at its core? And specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision
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, machine learning, and pattern recognition. The successful applicant will have: very strong programming skills and experience with deep learning libraries; strong python and object-oriented programming
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(MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers to quantitatively assess and characterize the state of a tumour and its microenvironment. This information
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representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
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approaches. What we ask of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow
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with low-data, sparse, or noisy datasets, typical in early-stage drug discovery. Technical skills: Proficiency in Python (required). Practical experience with machine learning or deep learning workflows
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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of the AMS Institute (Urban Energy, Metropolitan Food Systems, Mobility, Circularity, Climate Responsible Cities, Responsible Digitisation); A so-called T-profile, combining deep knowledge in the field