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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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acquire in the course of the PhD skills in live microscopy, experimental neurobiology, genetics and behavioural work. The student will receive mentoring and have the chance to guide the research and
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these processes requires AI and bioinformatics methods able to integrate heterogeneous, high-dimensional data. The ShadowEV-GBM project brings together spatial omics, metabolomics/lipidomics, molecular profiling
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information processing into an established consortium of experimental physicists and cancer biologists. The project establishes a close collaboration between IEMN and PhLAM, with the support of CANTHER
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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and signal processing, machine learning is now a global field supported by major international conferences such as AISTATS, NeurIPS, ICML, and MVML. Applying these methods to the study of Paleolithic
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objective will be to adapt tissue-clearing techniques for multimodal nonlinear imaging while preserving both the structural integrity of the biological specimens and the integrity of the optical signals
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d'Azur. The team is composed of computer scientists but has a strong interdisciplinary focus, particularly in linguistics, philosophy, sociology, and law. MARIANNE proposes innovative methods for natural
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neuronal circuits requires the coordinated execution of several developmental processes, including neuronal migration, axon growth and guidance, and synaptogenesis. These processes critically depend
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interactions with planets and their magnetospheres, and the broader topic of Space Weather. Today, most research in HP is data-driven, with data processing pipelines that produce time series, images, spectra