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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
. Such structures can be used to construct artificial cell mimics and new materials. A scaffold-free DNA tile assembly is a programmable method for the formation of two- and three-dimensional DNA structures which
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Completed university studies (Master) in Chemistry or related field Experience in metal-organic syntheses Experience in working with glove boxes and Schlenk methods Experience in X-ray diffraction techniques
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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glove boxes and Schlenk methods Experience in X-ray diffraction techniques and other structural characterizations Experience in working with radionuclides, esp. actinides and TRU elements is of advantage
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: Digital methods for inverse materials design are essential
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techniques is highly desirable and knowledge of basic microscopy methods is an advantage. Interest and/or experience in working at the interface between wet and dry lab is a plus. Interest in working
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cytometry methods Experience with cell culture techniques is highly desired Previous experience with genomics approaches and data analysis is a plus. Interest in working in a project with wet and dry lab
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electrolysis and fuel cells (SOEC and SOFC). By combining numerical modeling with data-driven approaches, you will identify optimized operating conditions and strategies to improve both steady-state and dynamic
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and designing sustainable P2X value chains. As a PhD researcher, you will contribute to the new stack designs for high-temperature electrolysis and fuel cells (SOEC and SOFC). By combining numerical
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods