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on collective dynamical phenomena of complex systems and networks with a focus on strongly externally driven systems; application to fields in physics, engineering and biology, developing a computational toolbox
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: university degree in chemistry or physics and profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials
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of 2D materials. The PhD study will be pursued within the framework of a cotutelle program between the Technical University of Dresden and Charles University in Prague. The successful applicant will join
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Manufacturing and Materials – REMM". Requirements: Very good to good university degree in engineering science, ideally in material characterization, mechanics, or simulation methods. High self-motivation with a
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Master’s degree in Physics, Chemistry or Computer Science, or equivalent Interest in Physics, Chemistry and Machine Learning Good written and spoken English Ability to work both independently and in a team
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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San Juan http://www.unsj.edu.ar/ Further Information about the Doctoral program in Electrical Engineering https://iee-unsjconicet.org/doctorado_ingenieria_electrica/ Doctoral program in Electronic
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the project partners at KIT and FZ Jülich Your profile Completed university studies (Master/Diploma) in the field of chemistry, physics, geosciences or computer sciences or related field Understanding
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Description This PhD project builds on ongoing research projects in the area of photocatalytic membranes at IAMT. Current work will be expanded to (select) novel materials and membrane preparation
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available in the further tabs (e.g. “Application requirements”). Programme Description The Käte Hamburger Research Centre “Dis:connectivity in processes of globalisation” (global dis:connect) at the Ludwig