27 machine-learning-and-image-processing-"RMIT-University" PhD positions at CNRS in France
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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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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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. The institute offers a multidisciplinary environment that bridges fundamental discoveries with applied preclinical research. In partnership with IMATHERA (Preclinical Imaging and Radiotherapy Platform
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computational research. In particular: • A high-quality imaging platform • A dedicated biocomputing hub that guarantees reliable data storage, management, and advanced analytical capacity. Our laboratory is
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Sadron Institute. The team works on themes related to the process-structure-property correlations of pi-conjugated materials for applications in organic electronics. The SYCOMMOR team has particular
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using the KM3NeT detectors • Data analysis from SVOM instruments as well as images from the COLIBRI telescope • Participation in KM3NeT shifts and service tasks (calibration, construction, processing), as
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correlations or more innovative methods of multivariate analysis and we anticipate here an opportunity of using machine learning that could help in predicting properties or classifying sources. A last step will
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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to the problem of thermal measurement at the nanoscale. This thesis is part of the ANTICHI (Advanced Nanoscale Thermal Imaging and CHaracterization Instruments) project, which aims to provide a versatile
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fundamental understanding of climate change and its impacts, extending to the development of prototype climate services co-designed by stakeholders and climate modeling experts. The goal is to accelerate