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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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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
Engineering at Aalto University explores synergies between nonlinear control theory and machine learning to provide formal guarantees on performance, safety, and robustness of complex systems. We are a part of
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
between nonlinear control theory and machine learning to provide formal guarantees on performance, safety, and robustness of complex systems. We are a part of the Finnish Center for Artificial Intelligence
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Biotechnology research at Aalto University. The lab will develop next-generation methods to evaluate, understand, and engineer biodegradation of polymers such as plastics, microplastics, and textile-derived
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allows the successful applicant to choose a flexible balance between advanced machine learning method development and exciting applications on molecular data, including biomedicine and drug discovery
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of novel methods and analysis of remote sensing data to monitor water table dynamics and biophysical properties of northern peatlands, from local to Holarctic scales. The position also involves participation
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research group of Professor Juho Rousu. The position allows the successful applicant to choose a flexible balance between advanced machine learning method development and exciting applications on molecular
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. The main research responsibilities include the development of novel methods and analysis of remote sensing data to monitor water table dynamics and biophysical properties of northern peatlands, from local
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methods of quantum information processing. Our goal is to achieve JoFETs by utilizing hybrid devices based on silicon and high-quality superconducting silicides. Remarkably, silicides hold promise