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Field
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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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probability, allow for the application of tools from probability theory to combinatorial problems and motivate the study of the typical properties of various combinatorial models, such as the Erdős–Rényi random
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scientific computing, to name a few. Modern LC applications rely heavily on accurate and efficient mathematical modelling of confined LC systems. Typical questions are - can we theoretically predict physically
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training is not essential. Experience with computational or mathematical modelling would be valuable. We are looking for a highly motivated candidate with the willingness to learn and an enthusiasm
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comprise all aspects of applied mathematics, from model development and mathematical analysis to numerical implementation and statistical estimation of models as well as software packaging. What to expect
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Data-driven predictions of dynamical systems are used in many applications, ranging from the design of products and materials to weather and climate predictions. Mathematical concepts from geometry
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, and how they will evolve under future global change. To accurately assess the combined impact of different greenhouse-gases on climate it is important to use models that integrate the underlying carbon
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industrial partner, this project aims to develop a novel modelling and analysis approach to address the mathematical and technical challenges of the fluid-structure interaction (FSI) mechanisms globally
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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
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) at Heidelberg University, the following position (75%) for up to 3 years is to be filled by 01.01.2026 PhD position (f/m/d) in Vector-Borne Disease Modelling The place of employment is Heidelberg, in the Climate