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Field
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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies
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(XAI) methods to improve the understanding of key drivers controlling peatland conditions and ecosystem functioning. The research project will primarily focus on implementing and merging analyses
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(XAI) methods to improve the understanding of key drivers controlling peatland conditions and ecosystem functioning. The research project will primarily focus on implementing and merging analyses
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, antibodies, and their complexes using techniques such as cryo-electron microscopy (cryo-EM), X-ray crystallography, and supporting biophysical methods. Responsibilities will include protein expression and
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: Extensive experience in machine learning methods, tools, and platforms. Proficiency in Python, with demonstrated software development experience. Hands-on experience in MLOps, including the design and
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assist with leading sectoral working groups to conduct formal gap analyses, solicit input from stakeholder groups, and lead the development of new methodologies for climate impact measurement. The project
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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Sujet de stage As a member of a multidisciplinary technological research team comprised of experts in software/hardware analyses through the application of formal methods, you will actively contribute
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. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. Marine