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Field
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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(inklusive Schnittstellenintegration) Erfahrung im Umgang mit LLMs (z. B. GPT-4) und deren API-Schnittstelle Grundkenntnisse in Natural Language Processing (NLP) und dialogorientierten KI-Systemen Fähigkeit
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doctoral candidates Cultural and linguistic preparation Welcome event Heidelberg University University location Activate map To activate the map, click on the "Show map" button. We would like to point out
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datasets. Developing and implementing data processing workflows to create high-quality visualizations and maps. Utilizing PIK's high-performance computing cluster for spatial and temporal data analyses
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development (e.g. user interfaces and APIs) Background in soil, ecological or environmental science Comfortable with/Accustomed to handling small invertebrates (mites, springtails) Good command of German
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occur, and how can the overloading of individual regions be counteracted? Your contribution to scientific analysis: Further develop existing energy system models in Python to accurately map and analyze
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and visual development of proposed solutions (e.g. mockups, database and API design) You support the software development with modern web stacks You will be involved in the integration and further
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. You will examine, for example, how security of supply can be realistically mapped in a sector-coupled energy system, which political decisions, crises, or technological developments need to be taken
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University of Paderborn Position ID: 2640 -RA [#26649] Position Title: Position Location: Paderborn, Nordrhein-Westfalen 33098, Germany [map ] Subject Area: Analysis and Geometry Appl Deadline