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exploitation of these solutions requires basic understanding of the complex interactions in the environment which shape microbial communities and their functions in soil and at the plant root interface. Hence
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recognition events to colonization strategies. Within this framework, the advertised postdoctoral project is specifically focused on complex data analyses of plants for improved symbiotic associations with soil
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at risk, and translate the complex multi-source data into real-time, evidence-based decisions and clinical applications. The project's overall goal is to establish a unified, end-to-end epidemic
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therefore expect you to have: Strong analytical and computational skills A passion for complex research and development projects Ability to work both independently and collaboratively in a dynamic research
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thereafter. Explaining complex AI models is a key challenge for ethically responsible AI. Explainable AI (XAI) research aims to provide relevant information to assist developers and users in analyzing AI
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: fluctuating renewable energy, dynamic electricity prices, and increasing system complexity. In the future, industrial energy systems will not simply run. They will understand themselves. They will learn from
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of battery test data and you are comfortable working systematically with complex technical problems. Experience with industrial collaboration and battery management systems will be an advantage, as
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analysis and you are comfortable working systematically with complex technical problems. Experience with industrial collaborations and working with EU granted projects will be an advantage, as will a
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published in international peer reviewed journals. Understanding of the atmosphere and its modelling is essential. • Model development of WRF-Chem is complex. Documented experience using FORTRAN, C
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science field (+MBA is an advantage) 15+ years of experience within: Research commercialisation Startup ecosystems or industry innovation Proven experience with portfolio and/or complex project management