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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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-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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familiarity with RF design, synthesis, FEM, and MoM simulations. Quick learning skills with the ability to grasp new concepts from literature are highly appreciated. Problem-Solving: You demonstrate a
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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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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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system What you bring to the table You are studying mechanical engineering, physics or a comparable subject Initial knowledge of FEM simulation with programs such as Ansys is an advantage Ideally, you have
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system What you bring to the table You are studying mechanical engineering, physics or a comparable subject Initial knowledge of FEM simulation with programs such as Ansys is an advantage Ideally, you have
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engineering using FEM simulation tools. Experience in cryogenics and the operation of dilution refrigerators. Proficiency in coding of control and analysis software (preferably in Python). Ability to conduct