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, particularly Python and ML libraries, and use of remote calculation servers. Knowledge of informational, library and archival issues in the digital age and/or mastery of traditional machine learning tools
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-dimensional Euclidean spaces - Machine learning - Distributed computing - Complexity (communication, queries, memory) - Continuous optimization The specific topic can be refined based on the candidates' skills
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, control, machine learning. Requirements Research FieldEngineeringEducation LevelPhD or equivalent Research FieldComputer scienceEducation LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD
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(Project IRP INANOMEP of the CNRS, France-Belgium). The synergy between non-linear optics, theoretical chemistry, and machine learning gives a pronounced interdisciplinary character to the project
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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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. The project involves data driven statistical approaches, such as maximum likelihood inference, Granger Causality inference and machine learning. Requirements Research FieldChemistryEducation LevelMaster Degree
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as machine learning are being developed, however all those fancy methods relies on the very basic information which is the charge reconstructed on each channel. The detector response was modeled
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the Subatech team is one of the pioneers, to control associated errors and biases. Similarly, we will continue exploring innovative deep learning methods emerging from pioneering work within this team
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computing centers. The recruited person will have the opportunity to attend various training courses during her/his thesis, either related to the subject of the thesis or to more general topics. A PhD
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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