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Field
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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related fields). Holding a PhD degree, obtained upon completion of doctoral studies. Programming skills (Matlab, Python, etc.). Ability to work with large datasets. Knowledge of basic statistics. Ability
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journals and presentations at conferences. - Collaboration with interdisciplinary research teams as part of the CERTAINTY project. - Optimization and documentation of modeling configurations for future
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postdoc, you will contribute to research and teaching within advanced modelling, optimization and design of composite and lightweight structures for applications such as wind turbine blades. Your work tasks
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experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and components to automate
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, acceleration, algorithms for optimal transport or generative AI). A good knowledge of Python programming (for AI, optimization) is highly desirable. Website for additional job details https://emploi.cnrs.fr
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altitude balloons, swarm-capable dropsondes, robotic ocean expendables, etc). Impact can be realized through numerical and AI modeling, heightened situational awareness, optimized sampling, and greater
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interaction research. The postdoctoral researcher will contribute to the design and optimization of biomaterials, their biological characterization and the functional validation of the developed models
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the country in which the doctoral degree was obtained. Knowledge of using and optimizing the cellular thermal shift assay (CETSA) to understand the activity of novel hit molecules compounds. Expertise