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Field
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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
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demonstrated ability to communicate and interact with a diverse range of stakeholders and students. Demonstrated knowledge in Quasi-Monte Carlo methods and/or finite element analysis and/or machine learning is
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experience with HHG sources are a plus but not critical. Knowledge of programing languages such as Python (and with finite element simulations specifically) would be also valuable.
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Loadability and robustness under dynamic or off-axis forces You will combine analytical modeling, finite element simulations, and experimental validation to explore new coupling topologies and materials
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distribution in wire based additive manufacturing • Calibration and validation experiments for modelling • Temperature monitoring techniques • Finite element analysis method • Reviewing literature, planning and
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modelling using Finite Element (FE) method and FE simulation software (e.g. ANSYS), (3) Model Order Reduction (MOR) methods for mechanical simulation (4) numerical algorithms and models, and scientific
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be
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neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be applied in close
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experience · Be a proficient programmer, including in Python, with good coding habits; experience in NEURON, COMSOL, other finite element modeling software, and / or git would be an asset; experience
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. Assets (Nonessential): Experience with FEA (Finite Element Analysis) and CFD (Computational Fluid Dynamics). Excellent skills with Solidworks, Inventor, and/or Siemens NX, or other solid modelling software