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, with the longer-term objective of improving the prediction of fast-charging behaviour. Your project will be to build or adapt a machine-learning interatomic potential for lithiated graphite using density
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integration scheme builds on a CEA-LETI patent that enabled the fabrication of TaN/TiN-based superconducting interconnections, exhibiting a critical temperature (Tc) on the order of one kelvin [1]. The goal
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international scientific journals and present your work at major conferences; · The possibility to contribute to technological innovation and patent development; · The opportunity to build a strong scientific
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, numerical systems designs. The project builds on complementary developments from both teams: TRAITOR for experimental fault injection and µArchiFI for formal securi Applicants should hold a PhD in computer
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, ...) ; -High level programming language: C++/Java/Scala ... - Scripting languages : Bash/Python ... - Development tools : IDE, make/cmake, svn/git, continuous integration, docker ... - Git environement Desired
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control system; Developing, testing and applying advanced data processing tools; Analyzing fluctuation and correlation measurements and comparing the results with theoretical predictions; Building databases
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where a threshold is applied to build a binary classifier to tell if a sample is in-distribution (InD) or OoD. In particular, the confidence score threshold is typically set using the values