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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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, numerical methods, or machine learning approaches is an advantage. Fluent command of written and spoken English is necessary; German is an advantage but not required. High degree of independence, motivation
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biophysics. Familiarity with simulation environments, numerical methods, or machine learning approaches is an advantage. Fluent command of written and spoken English is necessary; German is an advantage but
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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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and digitization. More than 400 employees – including around 100 students – from over 50 countries work at nine locations in scientific and non-scientific teams on the development of innovative methods
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and digitization. More than 400 employees – including around 100 students – from over 50 countries work at nine locations in scientific and non-scientific teams on the development of innovative methods
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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