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Student (f/m/d) in Quantum Algorithms for Droplet and Bubble Oscillation Dynamics Modelling. Your tasks Development and implementation of numerical and algorithmic methods for the simulation of fluid
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Functional Theory (DFT) Familiarity with artificial intelligence methods Good knowledge of electronic structure methods Experience with Linux, Git and related tools Knowledge in the field of high-performance
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, Process Engineering, Computational Science, or a related discipline Strong foundation in fluid mechanics, gas–liquid two-phase flows, numerical methods (FVM, FEM), and two-phase flow instrumentation
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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computational engineering, mathematics, computer science, physics, engineering or a related field Strong background in numerical methods and machine learning Proficiency in at least one programming language
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mechanisms using computer simulations and the methods of non-equilibrium statistical physics. The Research The PhD student will work under the supervision of Dr. Philip Bittihn within the European Doctoral
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of industrial processes. The Department Process Diagnostics develops highly precise measurement techniques, e.g., ultrafast X-Ray tomography, ultrasound techniques and inductive methods, to characterize
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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systems, including methods for colloid characterization Spatially resolved surface analysis using interference microscopy and autoradiography Derivation and parameterization of mechanisms Interdisciplinary