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two PhD students for this project. Your work focuses on integrating multiple sources for prediction, including common and rare genetic variants, family history, ancestry and typical age of onset. Your
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meets multiple of the following criteria: a Master’s degree in computing science, information science, or a closely related field; strong programming skills for implementing and testing computational
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negative caregiver outcomes. Methodologically, the project combines quantitative and qualitative approaches and builds on high-quality Dutch data sources. The PhD candidate will conduct multiple empirical
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hands-on methods to support the responsible, societally and ethically aligned advancement of animal-free technologies for biomedical translation and risk assessment. As a successful applicant, you will
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for AI/ML, IT, and software developers, cybersecurity professionals, and governmental actors. Align technical solutions with legal, ethical, and policy frameworks, including the Data Governance Act
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optimize algorithms and hardware. Co-optimization ensures that architectural decisions—such as token merging, sparsity exploitation, and quantization—are aligned with hardware datapaths and memory
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algorithms that align prefab factory production schedules with IWT capacity, terminals, and urban delivery windows. Model multi-level planning decisions, connecting early feasibility assessment and quotation
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multiple brain areas in an optimised way. This may include neural network and neural mass modelling of large-scale brain activity during and after stimulation, and experimental tACS in healthy participants
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- https://employment.ku.dk/phd/?show=155828 You may indicate up to three DC positions of interest. In case of interest in multiple DC positions, a single application suffices, unless this includes one
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bridging digital modelling with real-world factory implementation, this project will contribute practical methodologies and guidelines for scalable, circular manufacturing systems across multiple industrial