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from chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’
Vacancies PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’ Key takeaways In high-stakes crises, strategic teams often aim to avoid errors at all
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: 30,00 | Classification CBA: §48 VwGr. B1 Grundstufe (praedoc) Limited contract until: 30.04.2029 Job ID: 5311 The work group Machine Learning with Graphs of the subunit Data Mining and Machine
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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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records that can systematically inform preparedness, training and future response. As a result, learning from past events is fragmented, inconsistently captured, and insufficiently embedded into emergency
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invites applications for a PhD position focused on developing a theoretical framework for monitoring and updating adaptive learning systems (including machine learning/artificial intelligence systems) under
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Course: PhD studentship Project title: Exploring a One Welfare approach to human-animal-computer interaction in digital dairy technologies Principal supervisor: Charlotte Doidge Other supervisors
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits