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knowledge of energy system modelling or climate modelling Good knowledge of deep learning, PDEs or mathematical/numerical optimization methods Enthusiasm for challenging problems and interdisciplinary
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interest in developing theoretical models and methods as well as in implementing numerical optimization techniques Interest in working closely with experimentalists Detailed knowledge of quantum physics and
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models incorporating time-dependent source depletion. Reducing uncertainty in groundwater risk assessments through refined numerical methods. Applying the improved model to real-world groundwater
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documented experience in at least one and preferably more of the following areas: computational solid- and/or biomechanics; finite strain hype elasticity; finite element methods; numerical optimization methods
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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
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Cendón CALL DETAILS Tasks to be carried out: Collaborate in research tasks related to new mathematical models and numerical methods for the valuation of renewable energy certificates (RECs) and their
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in vacuum using a laser focused through a high numerical aperture objective. The laser produces an optical force equivalent to a mechanical spring, and the system can be regarded as a mass-spring
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innovative building technologies. You are passionate about exploring novel built environment control methods, with a particular focus on how to integrate smart materials into energy-efficient indoor air
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alarm signal or take other actions locally. We aim to implement methods for quality control and enhancement of data quality through approaches developed in other PhD projects within SFI Smart Ocean. These