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Field
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design
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/forschung/gruppen/numerical-analysis/research/ Typical responsibilities you can expect: Mathematical derivation, analysis, and comparison of models, methods, and simulation approaches Formal proofs, e.g
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the benchmarking of continuum modeling code together with GTK. Take part in dissemination, outreach, and teaching activities. Profile of the candidate PhD in relevant field (Physics, Chemistry, Geology, Fluid
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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academic and professional qualifications Proven research experience in the field of modelling and analysis of biological networks Solid foundation in mathematics and algorithmic design Strong programming
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project supported by the Challenge Programme of the Novo Nordisk Foundation: “Mathematical Modelling for Microbial Community Induced Metabolic Diseases”, led by Prof. Daniel Merkle. The expected starting
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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pathways driving leukemic transformation during haematopoietic development. The successful candidate will use in vitro differentiation systems (ESC/iPSC) to model normal and malignant haematopoiesis
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to build predictive models Collaborate closely with experimentalists and modelling experts Project Environment This position is part of a collaborative research project involving: Two PhD students at TU
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, and materials science. The successful candidate will have: PhD in biochemistry, molecular biology, protein sciences or a related discipline. Additional research experience in industry or a post-doctoral