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and carry out finite element method (FEM) simulations. Our developments focus on higher efficiencies, more cost-effective manufacturing processes and materials, improved long-term stability and new
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« group of the »Glass and Precision Manufacturing« department, are looking for motivated student assistants involved in highly innovative research projects. Your main tasks consist of FEM simulation and
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-time data acquisition during production, consulting, and prototype manufacturing. Graph neural networks provide an opportunity to operate on Mesh structured data utilized in Finite Element Method (FEM
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modeling (FEM), and thermodynamic/kinetic simulations using tools such as Thermo-Calc and CALPHAD first experience in applying artificial intelligence and machine learning techniques to Materials Science
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with FEM or CFD methods and tools such as COMSOL, ANSYS or similar software packages Ideally, experience with thermal and mechanical loads of high-temperature materials Scientific creativity, teamwork
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comparable field (bachelor's/master's degree) Interest in modern manufacturing technologies and welding processes Experience with Python/data analysis/FEM Structured, independent working style and ability
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shaping the change! We support you in your work with: A research topic with strong relevance for future memory technologies Work with a well-established FEM simulation tool Close supervision and integration
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methods/simulations, state-of-the-art computational techniques (e.g. data-driven methods and/or FEM) and/or theoretical material modeling will be given preference We offer: chance to collaborate with
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Some experience with numerical simulations such as the Finite Element Method (FEM) and Multi-Body Simulation (MBS) is a plus. We offer: a fascinating understanding at mobility in general with a
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or doctoral Degree in Communications/Electrical Engineering, Physics, or comparable disciplines Academic background with a focus on optical-wireless communication Experience with FEM-based electromagnetic