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Genome function and mechanics
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We investigate the physical properties of genomes and their function
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Physics of life and active matter
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Hydrodynamics and Turbulence
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Our team has a strong expertise on several aspects of hydrodynamics and turbulence, mixing theoretical, numerical and experimental approaches. We are focusing on internal waves and rotating fluid, acoustic spectroscopy of turbulence, particle motions in turbulent flows, thermal convection, MHD and plasma turbulence.
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Waves, Flows and Fluctuations
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Information processing and Phyics for social studies, biology, and health
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Specific applications are conducted alongside methodological works in physics and information processing, on selected domains: study of human activities (for social systems, in economics, or in geography), analysis in biology, especially in genomics, and biomedical signal or image processing.
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Information, Apprentissage, Numérique
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Information processing: Signals, Images, Graphs
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The group has a strong history of research in signal and image processing and other processing methods for heterogenous data. Key-words of our main contributions are: nonstationary signals, multiscale analysis, inverse problems, signal/image processing and machine learning, deep learning, graph signal processing, and network science.
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Information, Apprentissage, Numérique
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Home Made Instrumentation
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Our team has a strong tradition in developing new instrumentations. Cutting-edge instruments are developed in collaboration with our workshops and cover a broad range of subject including optical tweezers, instrumented particles, plasmon microscopy, ultra-low noise measurements, enhanced force AFM.
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Waves, Flows and Fluctuations
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Integrable Systems
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Theory & Models
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Integrable Systems & Mathematical Methods
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Theory & Models
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Scale invariance, multifractals and wavelets
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Scale invariance analysis and modeling has been a long standing research topic within SiSyPh team. Significant efforts have been and still are devoted to the definition, design and practical synthesis of stochastic processes with prescribed scaling properties, combined either to other statistic properties (distributions, dependencies) or to geometrical properties (free divergence, prescribed curl,...) to match a given application field (mainly hydrodynamic turbulence). Statistical signal processing tools are also designed and studied theoretically and practically, aiming to test the existence of scale invariance, to estimate scaling parameters or the scaling range bounds. The recent focus is on multivariate signals, and multidimensional fields with possible anisotropy.
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SIgnals, SYstems & PHysics
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Machine Learning, Computational Physics, and Numerical Studies
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Existing synergies between Machine Learning, computational physics and studies about numerical approaches are conducted in this theme. With that, we want to develop and promote AI for Science, as well as Science for AI, and integrate that in Computational physics and Data processing.
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Information, Apprentissage, Numérique
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Mathematical physics
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A. Alastuey, F. Delduc, M. Geiller, K. Kozlowski, L. Lionni, G. Niccoli, E. Livine, M. Magro, J. M. Maillet, H. Samtleben
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Theoretical Physics
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