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laser experimentation to study and control complex nonlinear dynamics. Key Responsibilities: Develop and implement Physics-Informed Neural Network (PINN) models to simulate, predict, and analyze complex
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. The dynamic nature of cell-cell adhesion, where binding and unbinding events occur continuously, adds complexity to the tissue’s mechanical response. Additionally, intracellular processes such as cytoskeletal
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. Our main research areas are neuroimaging data analysis (fMRI & EEG, iEEG, anatomical and diffusion MRI), brain dynamics modelling, causality and information flow inference, nonlinearity and
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dynamical structure directly from time-series data. This includes methodological work on nonlinear state-space reconstruction, system identification, reservoir computing and related recurrent architectures
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conduct researches of transport phenomena and dynamics of chemical reaction using the approaches of active matter, nonequilibrium statistical physics, theory/mathematical calculation method of nonlinear
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(neutral and doped quantum dots in micropillars, NV centers in waveguides, optical nonlinearities, etc.). Approaches may be analytical, based on the collision model, or numerical. The postdoctoral researcher
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CAS will provide the necessary expertise in the analysis of complex data, including multimodal neuroimaging measurements, the use of information theory, nonlinear dynamic systems, network theory
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to better understand the rich nonlinear dynamics when light, in the forms of our engineered superconducting cavities, interacts with artificial atoms based on Josephson junctions. Our research has
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optimise its role in maximizing fusion performance Analyse the linear and nonlinear dynamics of key micro-instabilities to identify the fundamental physical mechanisms governing turbulent transport Develop
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-resolution, high-speed, and deep 3D quantitative imaging. In collaboration with the Department of Clinical Medicine (UiT), the system will be evaluated for dynamic imaging of cardiac activity in engineered