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CFM-Centro de Física de Materiales/MPC-Materials Physics Center | Donostia-San Sebastian, Pais Vasco | Spain | about 2 months ago
to establish a closed-loop platform that combines computational inverse design, robotic synthesis, online characterization, and machine learning-driven optimization of plasmonic nanoparticles. Join us
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fields (PhD is preferred) and have a background in imaging, image analyses, machine learning, and software engineering. References: (1) A. J. Brooks et al, “Neutron interferometry detection of early
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«Detaljer» for å lese mer om dette og tilpasse dine valg. Om cookies Bare nødvendige cookies UiO/Anders Lien 9th September 2026 Languages English English English PhD Research Fellow in Machine Learning for
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AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities Develop machine learning methods and tools with a specific focus on: Data-Centric AI: Including
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topics: Development of deep learning models Mathematical foundations of machine learning Optimal deployment of AI systems The candidate is expected to hold a relevant MSc degree in Computer Science
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is expected between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects
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Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research sits at the
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, experience and attributes: MSc/MEng or PhD (desirable) in Machine Learning, AI, Computational Fluid Dynamics, Hydrodynamics, Optimisation, or a related discipline. Experience of applying machine learning
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Engineer will have the following skills, experience and attributes: MSc/MEng or PhD (desirable) in Machine Learning, AI, Computational Fluid Dynamics, Hydrodynamics, Optimisation, or a related discipline
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-preserving machine learning (e.g. proving model integrity or fairness without revealing training data) Scalable ZK-proof constructions for federated and decentralised AI pipelines Formal security analysis of