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theoretical ecology, geosciences, applied mathematics, environmental physics, or a related field. Research experience in numerical modelling, preferably with knowledge of either eco-evolutionary models and/or
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simulation, with strong skills in mathematics Highly proficient in Matlab/Simulink for system modelling Experience with (stationary) battery storage systems is required Prior exposure to HIL systems is a
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soon as possible. The position is within the Math+ project "Anisotropic microfluids -- fluctuations, control, effective models and their numerics “. Here, we study anisotropic microfluids and the effect
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for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The purpose of this position is to conduct research in the field of model
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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assigned to the research project "Randomization of Surrogates for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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. The Department of Mathematics and Computer Science, Hessain.AI research group “Artificial Intelligence – Deep Decision Support Systems”, led by Prof. Dr. Martin Becker, is currently accepting applications for a
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Join our team as a Early-Career PostDoc (m/f/d) in the research area of AI‑based modelling full-time (39.2 hr/week), fixed term for two years. Leibniz-IWT stands for research in the field of new
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(SDPs). You will design climate change mitigation scenarios that respect both climate targets and sustainable development goals using the REMIND model, one of the world’s leading integrated energy-economy