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implant loosening and wear/abrasion for anatomical and reverse TSR systems. This includes the use of interfacial BEM or FEM friction and wear models, as well as biological bone ingrowth models, to predict
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(e.g., FDTD, FEM, RCWA) Experience with data analysis tools such as Python or MATLAB Experience with 2D materials, polaritonic systems, or anisotropic optical media Experience with near-field optical
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1990, EN 1992, 1993, 1994 Experience with nonlinear FEM, preferably Abaqus, GMNIA Interest in combining laboratory and numerical research Language Requirements: Applicants must demonstrate at least B2
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on materials. Background (preferential): Materials science Additive manufacturing Metallic alloys Material characterization/Materials testing FEM/CFD simulations Additional background that will be valued in
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movement analysis, musculoskeletal modelling (e.g., Opensim, AnyBody) Experience in application of BEM/FEM computational methods Background in Tribology / Medical Devices Experience in advance mechanical
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analytical methods for electromagnetic characterization of lines and cables (characteristic matrices, FEM, MoM-SO, frequency-dependent earth properties, eigen-/modal analysis, wideband line models, etc
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7 Feb 2026 Job Information Organisation/Company CNRS Department Institut de Recherche en Informatique de Toulouse Research Field Computer science Mathematics » Algorithms Researcher Profile First
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if they demonstrate strong relevant skills. Coursework or strong background in computational mechanics / FEM, numerical methods, and scientific programming. Exposure to machine learning / data-driven modelling and/or
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in a wave tank under regular and irregular waves. Develop and document high-fidelity numerical models (e.g., coupled hydrodynamic–structural models and/or FEM workflows). Develop and validate
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) related to the basics of machine design, engineering graphics, CAD, FEM and other subjects necessary to complete the study program in the field of: machine construction and operation, technical mechanics