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numerical methods, as well as familiarity with concepts in complex systems, physical memories or machine learning. We strongly believe in the benefits of an inclusive and diverse research environment, and
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? No Offer Description Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15
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: Collaborate with other PhD candidates and researchers working on the project to share insights and learn from its different sub-projects. The successful PhD candidate will be based at the Faculty of Industrial
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will perform experiments, develop dose-response models and mechanistic understanding allowing to develop a workflow allowing understanding and awareness amongst different stakeholders. You will be
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, disability studies, (co-)design methods, and Human-Computer Interaction (HCI). The ideal candidate is passionate about creating socially impactful inclusive co-design methods and eager to collaborate directly
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Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15 doctoral projects
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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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approaches that integrate both qualitative and quantitative research (e.g., combining big data or machine learning with in-depth fieldwork) can also be pursued. Method selection and mastery are viewed as part
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security vulnerabilities. You will innovate the Find2Fix pipeline by making the different steps, including found issues and suggested patches, easier to understand using interpretable AI using state machine
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Find2Fix will engineer the first open-source tool for the entire process from software error discovery to mitigation based on modern active learning. Job Description You will research the state