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Field
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Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in numerical optimization and simulation models. Proficiency in one of the major programming
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Project advert Perfluoroalkyl substances (PFAS) are manmade chemicals that are ubiquitous within society. PFAS offer water/oil repellence and non-stick properties, featured in numerous consumer
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. Experience in implementing numerical methods and algorithms, e.g. in Python, Matlab or similar, is required. A strong motivation to develop mathematical tools for biological and medical applications is
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environments with minimal environmental impact. We are recognized nationally and internationally for our excellence in numerical and computational modelling, experimental innovations, our collaborations with
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science. Applicants with one or more of the following skills/qualities will be considered with priority: experience and/or thorough understanding of theoretical/numerical methods for simulating optical
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. the incompressible Navier-Stokes equations, and ideally have some experience in programming and numerical methods. English language requirements: Applicants must meet the minimum English language requirements. Further
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methods to be considered for numerical optimization by an Energy and Emission Management System (EEMS). Data-driven AI methods (e.g. Reinforcement Learning and/or Recurrent Neural Networks) to be considered
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applications in parametric modeling environments, including beam, shell, and solid-based elements. Creating a robust foundation in FEM theory and numerical methods, enabling the candidate to specialize in
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(university diploma or master's degree) in the field of geosciences, engineering or physics. Ideally, you have knowledge of numerical methods and experience with common programming languages (e.g. Matlab
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datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning). The goals are to develop new computational methods that allow the scientific inference