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Field
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to investigate electromagnetic wave propagation in time-varying THz waveguides. Job description Develop numerical simulation frameworks for time-varying electromagnetic systems, modeling ultrafast refractive-index
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copy or written statement is accepted if not yet completed) Publication list (if possible) Reference letters (if available) Application deadline: The deadline for applications is 15 August 2026, 23:59
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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will be exclusively accepted online at http://www.csuohio.edu/offices/hrd/employment.html . Mailed or emailed application materials will not be accepted. Hours per week 40 Work Schedule Monday through
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integrates Agent-Based Models, hydrological and flood models. GEB can simulate the dynamics between multiple hazard risk, how these risk impact society, vulnerability of different sectors, and how society can
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compare results with electromagnetic simulations; publish research results in leading international journals and present them at international conferences. Where to apply E-mail [email protected]
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on: https://www.dges.gov.pt/pt/pagina/reconhecimento Workplan and the objectives to achieve: The work to be carried out consists of the development and experimental validation of control and estimation
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: SAgE Find out more about our Research Institutes: https://www.ncl.ac.uk/ As part of our commitment to career development for research colleagues, the University has developed 3 levels of Research
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the following: Up-to-date resume. Motivation letter (maximum of 1 page). A copy of your (master) thesis, internship report or equivalent. Website for additional job details https://www.academictransfer.com/362472
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disentangle magnetic and structural contributions to electron diffraction data and to compare the results with measurements recorded using X-rays and neutrons. Simulation and interpretation of experimental data