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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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not exclusively deploy qualitative IS methods, with specific focus on case study research, taxonomy-building and design science research, as well as to conduct interviews with relevant stakeholders
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understand, explain and advance society and environment we live in. Your role Conduct research and prepare a doctoral thesis in metabolic network modelling and computational epigenomics Develop novel methods
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brief (around 500 W) sketch for a potential PhD project (research question, a short literature review, an overview over the methods and data that you aim to use), related to the design and topics
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the application of machine learning for official statistics. The subject of the thesis will be "Exploring Large Language Models for Data-to-Text Problems" and involves the study of technical methods and approaches
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed