172 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Forschungszentrum Jülich
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, energy systems, or material sciences A Masters degree with a strong academic background in mathematics, computer science, physics, material science, earth science, life science, engineering, or a related
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software openly with documentation. Your Profile: A Masters degree with a strong academic background in mathematics, computer science and earth science/engineering, or a related field Proficiency in at least
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international research sites Your Profile: Completed master’s degree in mechanical engineering, material science, physics or a related field Experience in modelling and simulation (e.g. Python) Willingness and
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, computer science, and social psychology Your Profile: Completed master`s degree followed by a doctorate in psychology, cognitive science or in a similar field of study Research experience in the areas of crowds
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training schemes for large models on high-performance computers like JUWELS and JUPITER at the Jülich Supercomputing Center. If you`re passionate about AI research and eager to make a real-world impact, we
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and individually, for example through training opportunities and the structured JuDocS program for doctoral candidates: https://www.fz-juelich.de/en/judocs In addition to exciting tasks and a
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engineering, process engineering, chemistry or a comparable discipline Knowledge of hydrogen and energy research is an advantage High motivation to complete the doctorate within three years Very good
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Scientific communication of the results (publications, conference presentations) Intense interaction with consortium Your Profile: Master and PhD degree in materials science, physics, chemistry, informatics
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comprehensive master`s thesis Your Profile: Currently enrolled in a Master`s program in Chemistry, Physics, Materials Science, Process Engineering, or a comparable field of study Interest in the research field
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only