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their dynamics. This will be carried out on a highly noisy incomplete data, posing a significant theoretical and computational challenge. Validate the developed methods in simulations and in real neuronal
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consists of several post-doctoral and PhD students with backgrounds in psychology, cognitive science, neuroscience, computer science, engineering, and physics. The focus of our research is to develop new
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
coupled historical and scenario climate simulations; and 4) the development of an emulator for sea ice model physics. The work is part of a larger project, M2LInES, covering eleven institutions
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well as the residency programs and clinical training of Humanitas University, an international Medical School that stands out for its real-world simulation-based approach to medical training. Humanitas Research is made
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doctoral studies / focus on solid state physics or chemical physics Professional skills: Development of ab initio methods Methodological skills: Python, Fortran, C, C++ High performance computing Good
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] Subject Areas: Multiphysics Simulation of Hemodynamics, Treatment, and Long-Term Perspective of Cerebral Aneurysms Using Lattice Boltzmann Methods. Appl Deadline: 2025/06/30 11:59PM (accepting
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and optimization of measurement-based quantum computing protocols for quantum simulation of quantum many-body models. Preference will be given to candidates familiar with the stabilizer formalism and
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also be expected to be highly collaborative and train and mentor undergraduates in scientific research projects. ESSENTIAL DUTIES & RESPONSIBILITIES INCLUDE: 1) Computer simulations of protein structure
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. Utilizing a combination of experimental and computational approaches, we develop and characterize novel functional materials and devices driven by robust nanoscale quantum effects. We are currently seeking a
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials