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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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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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deep learning / computer vision / biomedical image computing Excellent English; German is an advantage for clinical collaboration We offer Highly interdisciplinary, translational research environment
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) analysis • Research, development and implementation of deep-learning approaches • Network architecture search • Real-time image analysis • Establishing multi modal (video, thermography, acoustic, RFID
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | 20 days ago
learning, astrophysics of compact objects and binary’s formation scenarios, cosmography with gravitational waves (including dark energy, dark matter, gravitational lensing), and tests of gravity in
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You
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. Topic / Area: The positions are flexible in terms of research direction within 3D vision and graphics with a heavy focus on cutting-edge deep learning-based techniques. We are particularly interested in