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Max Planck Institute for Plasma Physics (Greifswald), Greifswald | Greifswald, Mecklenburg Vorpommern | Germany | about 1 month ago
-Institute for Plasma Physics in Greifswald offers within the division Stellarator Reactor Studies a PostDoc-position for a Physicist or Engineer (m/f/d) for the development of computational models
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. The overall aim of this project is to address these challenges by: Developing new data-driven and physics-based models of battery behaviour. Designing advanced BMS algorithms for real-time monitoring and
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educational programs, we are now seeking a postdoctoral researcher to work on privacy for data-driven models and high-dimensional data. The position is full-time for two years, starting on 1st September, or as
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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We invite applications from highly motivated and talented researchers for a 2-year postdoctoral position in advanced skin models, cellular immunology, and spatial transcriptomics, starting August 1
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modeling including graphical causal modeling and methodological foundations of the study of technology governance. The research will involve safety cases for AI systems as well as broader aspects of model
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. The successful candidate will be joining the Ultracold Quantum Gases group led by Prof. Dr.Leticia Tarruell . The Fermi-Hubbard model is a cornerstone model of condensed matter physics. It describes the physics
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experiments with quantitative microscopical analysis and physics-based modelling to understand how conifers solve the challenge of solute transport against the flow of water through the needle, and what
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modeling. Selection will be based on qualifications, relevant experience, skills, and education. The successful candidate should be highly self-motivated and independent in conducting research under general
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-carbon electric power systems, taking into account wake interactions between individual wind turbines. The project focus is on how to generate and utilize reduced-complexity predictive models for windfarm