122 assistant-professor-computer-science-data "https:" "https:" "https:" "https:" "Dr" scholarships at DAAD in Germany
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allowance Selection Selection is made by the National Science and Technology Council - NSTC in Taiwan. More detailed information https://www.nstc.gov.tw/folksonomy/list/ad854ec5-b3d9-4e50-a521-7919449925f2?l
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EUR for the return to Paraguay. Selection Crucial selection requirements: Above-average performances during the previous studies High quality dissertation or research Project Further information
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with the document issued in the original language. Submit the application online. Please note: If you have any technical questions or problems your local information and advice centres could not help you with, please
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, Computational Biology or similar, depending on the project(s) you are applying for) and who are qualified to pursue a doctoral degree. In justified exceptional cases, we also accept applications from individuals
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: monthly scholarship payments Selection Candidates are selected by a committee of Mexican and German scientists and academics. Further information Comecyt - Consejo Mexiquense de Ciencia y Tecnología Website
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-34Zipcode60322CityFrankfurt am Main Contact details Tel:+49 69 154008953 E-Mail: doctoral at fs.de Web: https://www.frankfurt-school.de/en/home/study/doctoral?lang=de Legal notice: The information on this website is
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StreetLansstraße 7-9Zipcode14195CityBerlin Contact details Tel:+49-30-838-52868 E-Mail: office at gsnas.fu-berlin.de Web: https://www.jfki.fu-berlin.de/en/graduateschool/index.html Legal notice: The information
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StreetLansstraße 7-9Zipcode14195CityBerlin Contact details Tel:+49-30-838-52868 E-Mail: office at gsnas.fu-berlin.de Web: https://www.jfki.fu-berlin.de/en/graduateschool/index.html Legal notice: The information
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Master’s degree (or equivalent) in a relevant discipline such as computer science, mathematics, physics, or data science. They should have strong analytical skills related to statistics, machine learning
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties