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of Economics and Business Administration with opportunities to complete a Master's degree course at a state (public) or state-recognised German higher education institution and to gain a Master's Degree in
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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
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or spike correlation patterns limited to local neural circuits or span across brain regions? Set up a network model to reproduce the main results and provide potential neuronal mechanisms. Existing
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international conferences Supervise student theses Your Profile: Excellent Master`s degree with a strong academic background in computational engineering, mathematics, computer science, physics, engineering or a
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application, please include a statement of research interest, CV, copies of exams, degrees and grades (transcript of records), a copy of your Master thesis (or a draft thereof), published articles or other
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(transcript of records), a copy of your Master thesis (or a draft thereof), published articles or other relevant material such as letter(s) of recommendation (if applicable). Our Offer: We work on the very
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to the portal in PDF format. Translations, if applicable, should be uploaded together with the document issued in the original language. Submit the uploaded application documents online. Please note: If you have
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by the time you commence your scholarship-supported study programme. What can be funded? In this study programme, you can complete a Master's degree course/postgraduate degree course leading to a final
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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