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often represented in large neural networks that are hard to analyze and whose decision processes cannot be interpreted by humans. To make this technology available without sacrificing safety concerns, we
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academic, clinical, and industry partnerships, as well as global networks, we translate biological insights into innovations for early detection, individualized therapies, and disease prevention. Founded in
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intelligently to make learning more sustainable and efficient, and a DFG-funded project on distributed optimization and scalable training of deep neural networks, including transformer architectures. We invite
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questions in the areas of self-supervised/label-efficient learning and explainability of deep neural networks (XAI) are being developed, particularly for use in biomedical applications. Further information
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 2 months ago
electrophysiology, functional imaging, or light/electron microscopy Familiarity with genetic and molecular biology tools Background in computational modeling of neural systems A demonstrated ability to secure
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, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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models and neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio
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(ML4Earth). AI methods, and especially machine learning (ML) with deep neural networks have replaced traditional data analysis methods in recent years. The Technical University of Munich (TUM), together