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holidays and weekends (e.g. between Christmas and New Year) Further development of your personal strengths, e.g. through an extensive range of training courses; a structured program of continuing education
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energy system model workflows Your Profile: Master’s degree in computer science, data science, natural sciences, economics, engineering, mathematics or a related field of study Huge interest in data
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personal strengths, e.g. through an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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quantum computing as well as experience with cryogenics, signal delivery, microfabrication, materials optimization, and microwave control are highly preferred qualifications Please feel free to apply
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opportunity to actively shape the transformation. You can expect a varied programme: A highly motivated working group as well as an international and interdisciplinary working environment in one of the largest
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strengths, e.g. through an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center
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) engineering, computer science, or a related field You have very good knowledge of energy technology, energy economics, and energy policy You have already gained initial experience in energy system modeling You
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however require a significant measurement effort to be estimated, especially in the high-precision regime relevant for fault-tolerant quantum computing. You will investigate recent advances in
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problems Proficiency in data analysis and programming using at least one statistical program such as R, Python, or similar programming languages Experience with GAMS, GTAP, and Exiobase is an asset. Skills