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hope to find new cryptographic powers by exploiting quantum phenomena. Code-based cryptography not only offers plausible security against quantum attacks; it also leads to practically efficient
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) invites applications for the position of Assistant Professor in Information and Coding Theory. We are seeking a highly motivated researcher to advance research in modern coding and/or information theory
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respond to this vacancy online via the button below. Online screening may be part of the selection. Diversity code ARCNL is highly committed to an inclusive and diverse work environment: we want to develop
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? If so, we invite applications for a postdoctoral position in a collaborative meta-science project on the effectiveness of data and code sharing policies in research-performing organizations
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TwenteCountryNetherlandsCityEnschedePostal Code7522NBStreetDrienerlolaan 5 Contact City Enschede Website http://www.universiteittwente.nl/ Street Drienerlolaan 5 Postal Code 7522 NB STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn
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Code7522NBStreetDrienerlolaan 5 Contact City Enschede Website http://www.universiteittwente.nl/ Street Drienerlolaan 5 Postal Code 7522 NB STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail
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with specialization in cognitive science, psychological methods or a related discipline. Extensive experience coding in R and Python (or related computer programming languages). Excellent written and
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(e.g., difference-in-difference research designs); experience with handling large (register and survey) datasets; experience with statistical software such as Stata or R, and coding; an affinity with
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, telephone: 06 - 5103 2533, e-mail: eileen.samshuijzen@ru.nl . For more information about HFML-FELIX, see www.hfml-felix.nl . Diversity Code HFML-FELIX is committed to a diverse workforce: we aim to foster
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diffusion models Familiarity with CT imaging workflows or radiological image-analysis pipelines Ability to develop reproducible research code in Python, preferably using PyTorch Experience with explainable AI