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Your Job: Strategic development of the research field on material availability in global energy system transformations, building on the content of the ERC Starting Grant Independent project
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Max Planck Institute for Heart and Lung Research | Bad Nauheim, Hessen | Germany | about 2 months ago
zebrafish, mouse, C. elegans, and more recently humans. This is a novel and quickly developing field of research where many open questions remain to be addressed including 1) the nature and trafficking
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 11 days ago
is available at https://population-europe.eu/ . The Secretariat is hosted by the Max Planck Institute for Demographic Research and situated in the centre of Berlin (see below). A prerequisite for the
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of cell and immunotherapy; the successful candidate is expected to play a key role in further developing this field at the Mannheim and Heidelberg campuses. In research, the W3 professorship for transfusion
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, one of the world’s foremost authorities in hair follicle biology and skin regeneration. We develop human-relevant, translational approaches to overcome the limitations of traditional models and bring
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learning, physics-informed neural networks, graph neural networks, transformers, convolutional defiltering methods, etc.) for the integration in multi-physics simulation codes You will develop code for and
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source of inspiration for innovative developments and scientific excellence, it contributes to shaping our society and our future. For over 35 years, the Fraunhofer Institute for Production Systems and
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channel coding for 6G The doctoral candidate will work fully at TUM as a regular TUM employee (full E13 position). This research will be done within a research & development project with a high-tech company
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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data