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invites one PhD researcher to join a research project on the use of “low-dose electron microscopy techniques for the investigation of two-dimensional material networks” working under the supervision of Prof
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who would like to work on defining a methodology for assessing the perception of space in interior architecture through 3D digital tools. The goal of this PhD is to craft a methodology for digitally
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maximized to achieve high accuracies. This PhD offers the unique opportunity to combine lab work with data analysis, to record novel datasets, and software development, to process and design novel data
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-regulatory network analysis approaches, developed and employed in the Malysheva Lab, are required to reveal these mechanisms. With the proposed project we aim to bridge this gap in our knowledge of cis
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Ages onwards. The PhD student will be able to define their own empirical focus within the broad confines of the project. You will publish scientific articles related to the research project. You will
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project takes a macro-historical approach to analyse conceptions and practices of peace in European history, from the Middle Ages onwards. The PhD student will be able to define their own empirical focus
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of applying the concept of deep learning. By training a convolutional neural network, in which detailed image simulations are used as an input, we expect to be able to reliably locate even the lightest atomic
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opportunity to combine lab work with data analysis, to record novel datasets, and software development, to process and design novel data strategies using existing and novel datasets. Overall, this PhD will
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statistical software packages such as STATA, SPSS, SAS or R, or willing to acquire such knowledge. You pay attention to details and act with attention to quality and integrity. You have a sense of independence
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issues and social questions. You have a solid background in quantitative research methods and data analysis. You have a good knowledge of statistical software packages such as STATA, SPSS, SAS or R