1,515 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions in Germany
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embedded in the Bremen Research Cluster for Dynamics in Logistics (LogDynamics) (http://www.logdynamics.de ). LogDynamics is a cooperative network of research groups from five different departments
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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found in our guidelines for review under the following link: https://uni-tuebingen.de/en/12271 The successful candidate will be working in a highly collegial and family friendly work environment. The
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 5 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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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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Additional information on beginning, duration and mode of study The starting date of the PhD project is flexible. The initial funding period is usually three years. Find out more at https://www.bgc-jena.mpg.de
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-time full-time Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline Applications are possible throughout the year. For more information, please see: https
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faculty at the Max Planck Society and at Oxford University cover all major areas in Computer Science. For more information about the research interests of individual faculty, please visit: http://cis.mpg.de
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on average expenditure: Rent, including ancillary costs (in Erlangen, Nuremberg and surrounding areas): 410 EUR Food: 198 EUR Clothing: 46 EUR Travel costs (public transport and/or car): 89 EUR Study materials
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of the Cottbus–Senftenberg research region and enhances its visibility. Through innovative research and new teaching and learning formats, BTU actively shapes the future. It contributes to addressing major