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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
and graduate students at UNC-CH. Training in physics is fundamental among the natural sciences because it provides understanding of the forces governing the structure of matter, from subatomic particles
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-relativistic 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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activities of the CRC TRR 358 „Integral structures in geometry and representation theory“ Recruitment requirements: • Scientific university degree (Master) • PhD in mathematics or related field • Scientific
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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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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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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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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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: 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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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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-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