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Postdoctoral researcher (M/F), synthesis of crystal phase heterostructures by Molecular Beam Epitaxy
optimize the growth of GaAs nanowires integrating crystal-phase heterostructures by molecular beam epitaxy (MBE). Contribute to scientific writing, presentation of results, and promotion of the project's
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part of the Forschungsverbund Berlin (https://www.fv-berlin.de/) and part of the Leibniz Association (https://www.leibniz-gemeinschaft.de ). You can find more details on the institute webpage: https
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interdisciplinary meetings and workshops to foster knowledge exchange and innovation. Your work is vital to advancing less invasive treatment options, reducing patient recovery times, and optimizing healthcare
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and Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group
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optics experiments will contribute to a better understanding of local annealing processes, with the goal of achieving optimized control over the final material state, taking into account its crystalline
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systems.1 Recently, during two PhD thesis, we optimized Sb2Te3 and Bi2Se3 layers to obtain significant nonlinear absorption. More specifically, the saturable absorption behavior obtained was the highest
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vibrations), and structural (migration of atoms) effects with an atomistic resolution. This can be achieved by self-consistently coupling molecular dynamics (MD), density-functional theory (DFT), and quantum
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Responsibilities: Conduct membrane related projects for desalination, decarbonization, resource recovery, etc. Assist in the design, optimization, construction, commissioning and operation of membrane-based
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for a Postdoctoral Fellow in Biophysics Your tasks In this position, you will work at the interface of experimental biophysics, structural biology, artificial intelligence, and pharmaceutical formulation
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‑space exploration, and on‑line operational optimization of power systems. Your tasks in detail: Become familiar with our previously developed neural network superstructure for learning iterative