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Field
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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assigned. 2. Prepares research reports and technical papers for publishing. 3. Investigates the feasibility of applying a wide variety of scientific principles and theories. 4. Maintains substantial
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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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of Research ExperienceNone Additional Information Eligibility criteria Required scientific & technical background : - Materials science (theory, practice), with emphasis on composites and CMC - Materials
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when information becomes available during active sensing maneuvers using tools from control theory (see Cellini et al 2024 preprint). We also work with flying quadrotor systems in a flight tracking space
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specific combinations of rhythmic patterns reliably elicit pleasure and the urge to move. Building on recent work in music theory, rhythm cognition, and music information retrieval, the project will develop
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Science The Institute of Political Science has a strong research tradition in various areas of comparative, Dutch and international politics, as well as political theory. The Institute has around 85
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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Council (VR). The project is centered around inverse optimal control/inverse reinforcement learning, both for continuous-time and discrete-time systems. In particular, we are looking for a strong candidate