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Field
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PhD Candidate, you will develop new analytical theories and methods for complex systems of partial differential equations and apply them to models for marine biofilm growth. Join a stimulating research
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techniques); giving mathematical proofs of their correctness and efficiency; building state-of-the-art implementations of these new techniques (e.g., by leveraging data-parallel functional array programming
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intensive care? Or which characteristics make a machine learning model flag a certain bank transfer as potential money laundering? The aim of this project is to develop new mathematical techniques that can
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a strong background in computational modeling, optimization, and systems engineering, eager to develop advanced design and simulation frameworks for battery pack topology and e-powertrain integration
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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PhD position within the EMBRACER project, focusing on the role of climate feedbacks, tipping points, and uncertainties in integrated assessment models (IAMs). You will work on improving
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-driven modelling and numerical mathematics leading to computationally fast methods State-of-X (where X is charge, health and/or function) estimation at the pack level. This requires developing
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, architectural engineering, (applied) physics, applied mathematics, or a related field is required. Strong mathematical and analytical skills are essential. Solid understanding of numerical modeling and signal
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The candidate based at CWI will have either a computer science or mathematics background, with a strong affinity for energy systems modeling, or an [electrical] engineering background, but with
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machine learning model flag a certain bank transfer as potential money laundering? The aim of this project is to develop new mathematical techniques that can explain AI's internal mechanisms by generating