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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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sequences. Development the analysis method and algorithm. 12 to 24 months: Photothermal, photoacoustic and hotspot characterization of several nanoagents with a significantly different hotspot effect
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some knowledge in classical AFM. The candidate must have a strong taste for experimental work (sample preparation, characterization) but also for data analysis via the development of codes using high
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already have GIS skills and basic level of programming enabling them to quickly learn and adapt new codes. Experience in image and topographic analysis for geomorphologic interpretation needs to be
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Interdisciplinarity, Synthesis, Analysis and Modeling Laboratory (CEISAM) Laboratory federates research activities in the Nantes region in the field of Molecular Chemistry. Located on the Faculty of Fundamental and
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characterization methods (basic skills) Analysis of biological fluids and culture media (basic skills) Ability to work independently on new topics. Strong analytical and synthesis skills. Ability to write reports
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requirements English level B2 (CEFR) Other requirements Knowledge of multiphase flows; strong skills in CFD; Familiar with CFD tools, preferably Ansys Fluent; skills in experimentation (fluid analysis, flow
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been devoted to these issues within the history of economic thought. Until recently, existing studies have focused primarily on the economic analysis of slavery and on the commercial benefits of empire
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on information and digital technologies (computer science, automation, robotics, artificial intelligence). Its aim is to develop methods for the representation, analysis, and control of systems subject to various
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latent coordinates; ii) Cross-modal alignment (e.g., canonical correlation analysis and Deep CCA) to align heterogeneous parameterizations and modalities in a shared latent space, iii) Operator learning