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Physics / General relativity , Gravitation Theory , Gravitational waves , particle physics around axions , Physics Beyond the Standard Model , Quantum Field Theory , Theoretical High Energy Physics
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academic institutions. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also, perform data analysis utilizing medical
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background in nonlinear optics, ultrafast photonics, and integrated photonics, alongside the ability to develop predictive models for optical materials and photonic devices. The successful candidate will work
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quantification and model calibration for decision-making Familiarity with interdisciplinary work at the interface of climate, environment, and health. Special Instructions Please submit the following materials
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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characterization of high-quality 2D ferroelectric materials (e.g. 3R-TMDs, layered ferroelectrics) using exfoliation, stacking, and ultra-clean heterostructure assembly techniques. Advanced optical and
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Multiphysics, FLAC / FLAC3D, Abaqus, ANSYS, MATLAB, or comparable tools, including experience implementing user subroutines or custom extensions for advanced material modeling or customized workflows. • Ability
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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models of impurity-host coupling and identifying optical signatures of correlated and topological phases. This position offers an opportunity for multi-institutional collaboration with experimentalists in
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and outstanding education are interlinked and equally valued. We are seeking to appoint a Postdoctoral Researcher in Computational Materials Modelling to join the Multiscale Materials Modelling Group in