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Field
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relative to the university’s broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent. Benefits Rutgers offers a
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Max Planck Institute for Nuclear Physics, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 1 month ago
strong links to theory groups at the institute and astrophysical interpretation and modelling work may also be included. PhD in Physics or Electronic Engineering. Knowledge of relevant technologies and/or
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) physics on ATLAS at CERN and for the experiments at the Electron Ion Collider (EIC) being constructed at BNL. Applicants should have a strong interest in and experience with the LHC, RHIC, or EIC scientific
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: Physics / Nuclear Theory , quantum information/computation Appl Deadline: none (offers accepted posted 2025/04/09) Position Description: Apply Position Description The Department of Physics
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spin-splitting of the electronic band structure due to their magnetic order (including the collinear altermagnets). Our goal is to provide a test of theoretical predictions in a selection of real
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structural and algorithmic graph theory. The purpose of the role is to contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby
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: Postdoctoral Position Location: Stockholm, Stockholm 10691, Sweden [map ] Subject Area: Physics / Condensed Matter Theory Appl Deadline: 2025/09/15 11:59PM (posted 2025/02/04, listed until 2025/09/15
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simulation methods, decision theory, uncertainty quantification, machine learning. Applications and areas of key innovation Image analysis, computer graphics, autonomous and assisted driving, 3D scene analysis
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in computational research Comprehensive understanding of quantum mechanics and electronic structure theory is critical Experience with CFD (e.g., the use of OpenFoam or ALDFoam) and microkinetic
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing