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Field
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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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(FEM). Expertise in computational mechanics using FEM/FVM, with experience in commercial or open-source modelling of fluid-solid systems. Understanding of unconventional gas production and coal
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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, or multi-physics simulation. Experience and skills · Ideally 3–5 years of experience (including PhD) in one or more of the following: o Finite Element Modelling (FEM), o Multiphysics
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conventional methods like nonlinear FEM, and comparing the results to computational observations. 3) Support the educational activities of the Pl through graduate student mentoring, selected lectures, and
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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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application of machine learning to biomedical data ï‚· Background in computational modeling for neurobiology, including finite element modeling (FEM) or system identification methods in medical applications
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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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, for example a 4-year bachelor's degree is accepted. Strong written and verbal communication skills in English Meritorious qualifications Powertrain mechanics, tribology Modelling (FEM, CFD) Materials