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options for students, such as canoeing on the Lahn river, walks and picnics in its multiple green areas, cinemas with student concession tickets, the municipal theatre with a programme consisting of plays
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opportunity to contribute to cutting-edge research that has a real-world impact on the agricultural sector. The Analytics for the Australian Grain Industry (AAGI) Scholarship Program (AU node) is funded by
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research in TRL forward. Theoretical knowledge of, or experience with, machine learning such as representation and generative learning, and natural language processing. Programming skills, e.g. Python, Java
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/or human-computer interaction. Programming skills, e.g. Python, Java, or C++. Excellent command in English, verbal and written. Prior experience as a research assistant during (under)graduate studies
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at the heart of multiple Sustainable Development Goals. All around the world, however, water systems are increasingly under pressure. In urban areas, diverse human water demands are rising, e.g. for irrigation
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the value of the green transition. The project will involve a multi-stakeholder innovation process, utilizing a framework of multiple-loop learning to encourage farmers to reflect on their relationship with
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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well as for the concrete planning and management of neighborhood and building solutions. As part of our mission to develop smart solutions to tackle the climate change, we have launched a PhD programme with TU Munich
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et al., 2020). It provides a single-session, universal intervention delivered to between 10 and 200 individuals at a time, and it contrasts with other online programs that are formed from multiple
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disciplines. For details please see the programme information for the Philipp Franz von Siebold Award. Programme information (PDF, 119 KB) List of award winners The sponsorship The award amount is €80,000