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validation; proficiency in Python and scientific computing, including experience with finite element methods, preferably using FEniCS or DOLFINx; strong analytical and communication skills, with the 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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finite element analysis, finite difference schemes, or Monte Carlo simulations. Mentoring Assist in mentoring graduate and undergraduate students in related projects. Collaborate with faculty and students
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working in radiological environment. Experience in heat transfer and thermal modeling/simulation using finite-element analysis (FEA) or other software. Ability to work within a multi-disciplinary team
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modeling for neurobiology, including finite element modeling (FEM) or system identification methods in medical applications About the Department The Department of Biomedical Engineering is an academic unit
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record in theoretical and/or computational mechanics Knowledge, Skills, and Abilities Excellent analytical and mathematical skills Proficiency in numerical analysis using finite element method (FEM
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, or a closely related field with expertise in one or more of the following areas: Finite element methods for partial differential equations Multiscale numerical methods Flow and transport in porous
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(MPM), or advanced Finite Element Methods). Physical modeling of tunnel excavation and ground response (e.g., geotechnical centrifuge testing, lab-scale TBM experiments). Probabilistic and reliability
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within Irksome. A Ph.D. in applied mathematics, computer/computational science, or a related discipline and knowledge of finite element methods and scientific computing is required. Prior experience with
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of simulation tools (e.g., Multiphysics finite element analysis, Matlab, Labview etc.) cleanroom experience, and characterization of electronic devices are required. Further, knowledge of system level integration