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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
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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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additional professor plus an international tutor of the RTG Description of the PhD topic: This PhD project focuses on the development of fail-safe, distributed digital twins (DTs) for urban air mobility
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microscopy and traction force microcopy, as well as data analysis, development of theoretical models, and preparation of data for presentations and publications. We are looking for a motivated candidate, who
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.) disciplines. GönomiX: Focuses on the evolution of gene regulatory networks and Wnt pathway functions in emerging model organisms (e.g. insects, annelids, snails, flatworms, cnidarians), utilizing ‘omics
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development and deployment of the technology will ensure EU's leadership in the exploration and exploitation of deep space, the next commercial space frontier. The program is designed to achieve the following
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physiological processes of plant growth, development, reproduction and interaction with other organisms. In a plant’s life, interactions occur at multiple levels: among others, between its plastids and the
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. Successful and rapid development and deployment of the technology will ensure EU's leadership in the exploration and exploitation of deep space, the next commercial space frontier. The program is designed
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/m/d) Development of soft-sensors connected to particlebased separation models to control flotation processes. Your tasks Develop and implement soft‑sensor concepts for continuous monitoring of ore
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science, urban studies, and aerospace. Skill Development: Our extensive qualification concept goes beyond research, offering targeted training in drone technology, data analytics, regulatory aspects