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the project will be the assessment of the model results with atmospheric measurements, as well the use of statistical inference methods for hypothesis testing. Finally the improved model will be used to provide
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This PhD project will focus on developing AI-based methods to accelerate the Swansea University in-house discontinuous Galerkin (DG) finite element solver for the Boltzmann-BGK (BBGK) equation
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qualifications and experience. Area of expertise: Thermoplastic composite materials, composite material durability, test methods for composite materials, composite materials manufacturing, experimental
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for classical imagery inspections (e.g. CCTV) in sewer pipes treated with CIPP lining employing powerful semi-analytical and hybrid (numerical+analytical) acoustic simulations. During the project lifetime
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unmatched economic and social impact across the state by providing numerous academic and professional opportunities. Below you will find the details for the position currently available at UACCM including any
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supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. Responsibilities: -Develop and implement advanced numerical algorithms and deep learning
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demonstrate these in their application materials Familiarity with numerical methods for PDEs (e.g., finite difference or finite element methods) Experience with tissue simulations and/or HPC is a plus Interest
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Overview In 2024 we carefully quantitated a method allowing the probing of some aspects of the physiological status of a honeybee colony (see References ). The method consists of providing
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overall or higher or equivalent. Have an experience with analytical and numerical solution methods for partial differential equations and working knowledge of Matlab and Latex. A full list of selection
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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | 12 days ago
discretizations and/or machine learning methods. The ideal candidate should have a strong background in numerical analysis, scientific computing, and/or scientific machine learning. We are particularly interested