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PhD Studentship: Robust, Certified, and Scalable Federated Machine Unlearning for Privacy-Preserving AI About the Project As federated learning systems become increasingly embedded in high‑stakes
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is available to address a fundamental question in developmental science: how does shared reading between caregivers and young children support early learning, and what are the interpersonal neural
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Are you interested in learning more? Read about Umeå university as a workplace Description of work About the
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of the offer. The ideal PhD candidate will have: A strong background in machine learning, deep learning, and software programming Proficiency in Python and machine learning frameworks such as PyTorch
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English language requirements . Not have previously completed a PhD. Be able to commence the Program in the year of the offer. The ideal PhD candidate will have: A strong background in machine learning, deep
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characterizing individual nanoclusters • Engineering and purify protein nanopores with tailored sensitivity to size, charge, and etc. • Developing data analysis pipelines and machine learning approaches for signal
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to characterize forests and their biodiversity. The research will focus on developing multimodal learning approaches that combine complementary forest information across data sources, spatial scales, and time. A
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collaboration with internal and external partners, predictive models including machine learning (ML), materials thermodynamics, etc. for high-throughput identification and screening of advanced materials for gas
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learning systems. They will evaluate systems whose behaviour keeps moving, and track, from the inside, whether the structures that carry their capabilities and safety properties hold up under the change. You
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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest