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addition to technical models allowing to assess technical scaling and an optimal operation. The optimal plant design is identified by considering the energy supply system, the choice of capture technology, the DAC
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language. Programme duration 6 semesters Beginning Winter and summer semester Application deadline Varies, please check our website Tuition fees per semester in EUR None Combined Master's degree / PhD
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German Cancer Research Center (DKFZ) Heidelberg • | Heidelberg, Baden W rttemberg | Germany | 4 days ago
the fact that PhD students have an academic background in (molecular) biology, (bio-)chemistry, (bio-)physics, mathematics, computational biology, computer science, epidemiology/public health studies, and
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was established in 2019 with our partner institute, the Nagoya Institute of Technology, Japan, in order to better understand lead-free perovskite materials for photo-electro-mechanical energy conversion systems
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benefits undertake multi-disciplinary research & training in integrated photonics, spectroscopy and protein science be awarded a Ph.d. degree from 2 universities (double degree/cotutelle) gain industry
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equivalent degree with a strong background in neuroscience, biology, physics, biomedical sciences, computational science, applied mathematics, statistics, artificial intelligence, psychology, cognitive science
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Completed university studies (Master/Diploma) in the field of mechanical engineering, physics of related disciplines Enjoy experimental work in international and multidisciplinary teams Communicative
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available in the further tabs (e.g. “Application requirements”). Programme Description In line with its statutes, the Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation) supports
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of methodologies, from in-depth behavioral assessments to computer vision, machine learning and neuroimaging techniques, we aim to uncover the complexites of neurodevelopmental disorders. Our
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ranges from core areas of computer science and electronics over medical applications to societal aspects of AI. SECAI’s main research focus areas are: Composite AI: How can machine learning and symbolic AI