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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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structural health monitoring of structures immersed in heavy fluids, by continuing to develop the “Modal Strain Energy” and “Matched Field Processing” methods for detecting and locating a potential defect in
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images obtained by experimental techniques from numerical flow simulation databases using linear and non-linear optical equations. This area of investigation has a vast field of application, and the SimBI
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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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, including 180 permanent staff (researchers, professors, engineers, technicians, and administrative personnel) and around 180 non-permanent staff (PhD students, postdocs, and fixed-term contracts). Each year
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, including basic processing of photoemission data, as well as more advanced processing for HAXPES data or photoemission imaging. *Participate in measurements at large-scale facilities such as synchrotrons
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adipose tissue. In particular, we will study the role of different membrane receptors and their signaling pathways in the browning process. The various techniques used will include cell biology and genetic
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infrared, historically encompasses astronomy, imaging, remote sensing, chemical spectroscopy, as well as linear and nonlinear condensed matter studies. For all these applications, broadband THz sources
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), I³ (Inorganic–Isotopy–Imaging), ECOMES (emerging contaminants, speciation, and omics), Surface and Interface Characterisation, and Microbiological Characterisation. Together they enable an end‑to‑end
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process for the transformation of lignin rich biomass to aromatic aldehydes as value-added chemicals. The objective of this PhD project is the development of heterogenous catalysts for the selective