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Field
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quantum attacks Verifiable and privacy-preserving machine learning (e.g. proving model integrity or fairness without revealing training data) Scalable ZK-proof constructions for federated and
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, machine learning applications, and analyses of individual disease trajectories. Your tasks will include: Integrate multimodal neuroimaging datasets from MRI, PET, EEG, and fNIRS studies Characterize brain
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a
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integrated modeling and machine learning for energy and circular-economy applications. The Post Doctoral Associate will design and execute simulation and machine-learning studies, analyze results
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<span lang="en"> Helmholtz-Zentrum Hereon</span> | Geesthacht, Schleswig-Holstein | Germany | about 15 hours ago
Material and Process Design at the Helmholtz-Zentrum Hereon is offering a 4-year PhD position in the field of computational materials engineering, machine learning, and digital twins for advanced
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<span lang="en">Leipzig University</span> | Leipzig, Sachsen | Germany | about 15 hours ago
energy that drives the particles
- Track hundreds of interacting particles with machine-learning methods and test fundamental thermodynamic bounds on control
- Work closely with the Tel
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clinical data, imaging, pathology and outcome data Multi-agent and predictive AI development: Develop machine-learning components for patient-trajectory modelling, recurrence and survival prediction
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Chemistry and Materials Science is looking for: A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians The ELPH-ML project, led by Dr
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, generative resynthesis attacks, adversarial attacks, and realistic downstream processing such as mixing. Publishing research in leading journals and conferences in speech, audio, and machine learning
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and apply AI and machine-learning methods to infer interaction networks from acoustic co-occurrence and activity patterns, and analyse their structural properties to link topology to response diversity