69 structural-engineering-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions at Forschungszentrum Jülich
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well as spectroscopic methods to determine their composition, structure, and oxidation-state distribution. In addition, variable temperature and pressure studies will be carried out to probe their structural stability
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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. or equivalent) in applied mathematics or in computational engineering science, computer science, simulation science with a strong background in applied mathematics Excellent programming skills (Python, C/C
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work Your Profile: Completed university degree (Master`s) in a subject with a strong focus on chemical engineering, e.g. process engineering, mechanical engineering, technical chemistry, relevant
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
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on the campus of Forschungszentrum Jülich. Your tasks include in detail: Construction and commissioning of a new test stand for the investigation of the continuous butanediol dehydrogenation under dynamic
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to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so-called lean alloys, and material systems with a high tolerance to impurities from
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of experiments, measurement methods and measurement technology, including test facilities with regard to the research and project objectives Collaboration in the analysis of operated SOC stacks through disassembly
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processes based on CO2 and other carbon-rich waste streams enable net-zero CO2-conversions into valuable chemicals. This project aims to design, integrate and optimize metabolic pathways in an engineered C
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange