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differences in learning, memory, and processing between these systems. This project develops the necessary methods to study how smart AI-models are compared to people, now and in the future, and sheds light on
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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. Methodological Approach Candidates will develop and apply state-of-the-art machine learning techniques, including deep learning, representation learning, variational autoencoders, and graph-based models. A strong
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learning video-AI models; b) assess representational alignment of bio-inspired deep learning models to the human brain. The bio-inspired models will be enriched with different temporal integration mechanisms
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storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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limited. To learn from past warmer climates and better understand the link between climate and extremes, we can use proxy-based climate reconstructions and climate models for past warmer climates. However
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machine learning packages (e.g.PyTorch). Completed academic courses in AI or machine learning. Interest in societal, ethical and philosophical questions. We consider it an advantage if you bring one or more
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artificial intelligence, computer science, engineering, mathematics, physics, or a related discipline Demonstratable background in machine learning, information retrieval or natural language processing
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and scalable. Design and build a technology demonstrator prototype of clinical-testing grade. Collaborate with interdisciplinary teams, including clinicians, engineers, and machine learning (ML) and