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Field
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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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) 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 | 22 days 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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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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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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We seek outstanding candidates for a postdoctoral fellowship to work on condensed matter theory at the Center for Quantum Devices, Niels Bohr Institute, University of Copenhagen. An early starting
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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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: 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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-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
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in computational surface science, electronic structure theory, computational materials discovery, or machine learning in materials science. Research competence and initiative proven through