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. Fujii, K. & Nakajima, K. Harnessing disordered-ensemble quantum dynamics for machine learning. Phys Rev Appl 8, 024030 (2017). 2. Rudolph, M. S. et al, Generation of High-Resolution Handwritten Digits
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the development of the off-line trigger pipeline that will complement the onboard trigger software by coding new machine-learning-based analysis routines to help identify sources of interest for SVOM follow-up in
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medical physics. - Knowledge about medical imaging and machine learning would be a plus. - Good practice and knowledge of programming or prototyping softwares - Willingness to get involved in the medical
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, deep learning-based image methods have emerged as a prominent tool in medical image processing. While they have shown impressive success in various computer vision tasks, their application in the medical
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or as materials for transportation. Intensive calculations within the framework of density functional theory (DFT) will provide the basis for building machine-learning models to explore the range
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physics or computer science, with a solid background in AI/machine learning techniques. A background in plasma transport phenomena as well as an experience with data analysis, statistical methods, and
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materials or molecular modeling and will have skills in algorithmic programming (python required, C++ would be a plus). Experience in the field of machine learning will be appreciated. Additional comments
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or oceanography. Research background should demonstrate competence -- or at least a clear and strong interest -- in artificial intelligence and machine learning to be applied in the field of environmental sciences
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post-doctoral researcher position for a two-year postdoctoral fellowship on ATLAS, a mid-aged experiment at the Large Hadron Collider. The position aims at developing machine-learning-based tools
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interferometry (lasers, optics, electronics) -Knowledge of machine learning, optimal control, artificial intelligence Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8630-CARGAR-015