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Field
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degree in Engineering and have an interest in and/or a good understanding of numerical modelling and testing of structures. Prior knowledge of finite element methods and programming (e.g. C++, Python
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interdisciplinary environment within EMBRACER and international partners and apply advanced methods like Lagrangian tracking and reanalyses to reveal new insights into atmosphere-ice-ocean feedbacks in the Arctic
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further developing both the experimental and data analysis methods that are currently used within the research team. The student will learn how to use the MMI apparatus, gaining knowledge of, for example
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and very good knowledge in quantitative and qualitative research methods Good knowledge of statistical software (e.g. SPSS or STATA or R or JASP) Strong commitment and the ability to work in a team
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Master in Experimental Psychology, HCI, UX or related fields Experience with research methods in social sciences (e.g., empirical research, experiment design) Experience in statistical methods is
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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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), sustainable, and climate-adaptive crops. By combining plant biology, simulation modelling, and artificial intelligence we aim to develop smart breeding and cultivation methods. Thus, we try to speed up
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of the Dutch NWA consortium “PRELIFE”, https://www.originscenter.nl/prelife/ The origin of life remains one of science’s most profound and enduring mysteries. Despite numerous theories, no single explanation has
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-dependent source depletion. Reducing uncertainty in groundwater risk assessments through refined numerical methods. Applying the improved model to real-world groundwater contamination case studies. Career
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materials. Another major challenge is enabling motors to function in aqueous media, which opens up numerous opportunities for integrating motors with biological systems, i.e. to design responsive biohybrid