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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts
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develop models that can read, extract and acquire knowledge from legacy data, coming both in the form of text and in the form of structured data (e.g. physical measurements) to predict characteristics
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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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We’re seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon
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learning has strong potential for computer vision, from hyperbolic image segmentation [2] to hyperbolic tree embeddings [3] and hyperbolic vision-language models [4,5]. [1] Nickel, Maximillian, and Douwe
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a strong background in computational modeling, optimization, and systems engineering, eager to develop advanced design and simulation frameworks for battery pack topology and e-powertrain integration
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fully funded PhD position within the LowDataML doctoral network, focusing on developing innovative machine-learning approaches for drug discovery under low-data conditions. LowDataML aims to bridge
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-modal representation learning, and self-supervised learning for this novel perception task. The developed models should provide holistic representations of all surrounding traffic by fusing multi
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to hyperbolic tree embeddings and hyperbolic vision-language models, see this webpage for further details on the papers. Your goal will be to bring hyperbolic deep learning for computer vision to the next level