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to conduct research independently, have potential to publish in high-quality scholarly journals, and to teach competently. The position requires fluency in English. The Postdoctoral Researcher is required
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to conduct research independently, have potential to publish in high-quality scholarly journals, and to teach competently. The position requires fluency in English. The Postdoctoral Researcher is required
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to work on the discovery of new superconducting materials with high critical temperatures, using novel methods and concepts such as machine learning and quantum geometry. The project is related to large
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the demanding environment of submillikelvin temperatures and will learn about fertilizing connections between different areas of physics. The position is full-time. Contract will initially be made for two years
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
from all backgrounds to join our community. The Nonlinear Systems and Control group is seeking a talented and ambitious Postdoctoral Researcher to develop machine learning-enabled approaches
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opportunities at Aalto University means you will grow and learn, having the chance to participate actively in staff trainings and development projects based on your interests and needs. We work in a hybrid way
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tasks also in the future. Our vast array of professional development opportunities at Aalto University means you will grow and learn, having the chance to participate actively in staff trainings and
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healthcare. Our vast array of professional development opportunities means you will grow and learn, having the chance to participate actively in staff trainings and development projects based on your interests
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taking the role of corresponding author. Besides the expertise in experimental quantum optics, the candidate will acquire excellent training in grant writing and research leadership provided by Aalto
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and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game