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Field
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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analyze multi-omic data provided by our collaborative partners and interpret the results from a biologically relevant perspective. Planned activities include Python code development, the analysis and
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communicate research on the activity patterns underlying integration in the visual cortex. Activity: Acquisition and analysis of electrophysiological data, oral and written scientific communication. Where
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neuroscience and signal processing, the role centers on an existing analysis pipeline: refining it, applying it to new patient data, developing it further, and making it sustainable and transferable so that
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- Time-resolved single-particle cryo-electron microscopy: grid preparation, data acquisition and analysis, model building and analysis or Time-resolved serial X-ray crystallography: microcrystallization
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engineering and protein biochemistry is essential. Experience in -omics methods, as well as in protein production and characterisation (FPLC, WB, etc.), is required. Skills in the analysis of omics data and the
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structures of the future tracker. These activities will be carried out at IP2I. The analysis will mainly focus on the search for CP violation in the top quark sector with data from Run 3 of the LHC
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interpret dynamic visual signals. The successful candidate will contribute to experimental design, computational analysis of behavioral data, and the integration of biological insights with robotic systems
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materials. Materials for aerospace or space applications. Knowledge of experimental data analysis and scientific programming/software would also be considered an asset. Specific Requirements Key
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to AESEC, the experimental implementation of measurements in extreme environments, the execution of experimental campaigns, the analysis and interpretation of the resulting data, and the dissemination