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mechanics, applied mathematics, biomedical engineering, computer science or a closely related discipline Strong background in finite-element methods, continuum mechanics and numerical analysis Excellent
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(photolithography, metal evaporation, etching) Experience with packaging schemes such as flip-chip bonding, anisotropic conductive film bonding and wire bonding Finite element Method (FEM) simulations (MEMS, Electro
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mechanics, FEA, CFD, or equivalent field. Proficiency in one or multiple finite element analysis software (e.g., ABAQUS, LS-Dyna, ANSYS), and in computational fluid dynamic software (e.g., Fluent, CFX
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Linear and nonlinear photonic device testing Finite element analysis (FEA) Micro‑ and nano‑fabrication Knowledge in quantum optics/physics is a strong plus Qualifications: PhD required A combination of
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computational fluid dynamics (CFD), cardiovascular modeling, or biomechanical growth and remodeling. Demonstrated experience with numerical methods (e.g., finite element method), programming languages (C
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. Proficiency in CAD/CAM and finite-element modeling is required, alongside disciplined verification/validation practices and the ability to translate prototypes into reliable, user-ready systems. Demonstrated
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, anisotropic conductive film bonding and wire bonding Finite element Method (FEM) simulations (MEMS, Electro-static and Quasi-static simulations) Discrete electronic design (Analog and digital design using COTS
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rotor motor. You will do research on noise and vibration reduction due to the harmonics injected in electric machine due to switching converters. You will use advanced finite element analysis techniques
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%). You will work on the extension of the DUNE-FEM package to support computations on GPU hardware with various types of Finite Element methods. This work is embedded in a research project considering
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codes, finite element or finite different methods, peridynamics, phase field models, multi-objective optimisation methods, CAD. Demonstrated ability to adapt to fast-changing project direction and learn