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of advanced functional imaging methodologies for whole-brain mapping of neuronal activity (such as calcium functional imaging, nonlinear microscopy, and light-sheet microscopy), in parallel with monitoring
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aims to develop hybrid quantum–classical approaches for modeling multiphase flows governed by complex, nonlinear dynamics across multiple scales. The postdoctoral researcher will investigate how
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diffraction tomography, with a focus on inverse multiple-scattering algorithms. Implement and evaluate both linear approximation models and nonlinear high-order scattering approaches for accurate imaging
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systems. However, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification. Typical identification algorithms
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, when dynamics are complex, nonlinear and partially unknown, such a model is typically obtained from observations by performing system identification -- one notable example is given by Gaussian process
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
. Scientific Environment The Nonlinear Systems and Control group (https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/nonlinear-systems-and-control ) in the School of Electrical
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
Control group (https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/nonlinear-systems-and-control ) in the School of Electrical Engineering at Aalto University explores synergies
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PostDoc/Senior Scientist - Process and Plant Design in the Field of Liquid Organic Hydrogen Carriers
languages (e.g., Python, Julia, Matlab) Strong interest in process modeling and simulation, including novel methods and approaches such as neural networks and nonlinear optimization Ability to analyze complex
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improvements. Examples include optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques
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have been developed in the linear framework for several decades, the current challenge remains their extension to the nonlinear framework, which is necessary for an accurate description of complex