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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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description The project focuses on the development of AI methods for enabling the detection and editing of gender biases across cultural contexts (e.g., languages, gendered expectations, imageries) under
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for hypergraphs and partially ordered sets (POSets), funded by the Swedish Research Council. This project is concerned with saturation problems for two classes of combinatorial objects: hypergraphs and posets
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to 20% of their time, thereby prolonging the duration of the program to guarantee four years of graduate studies. More information about doctoral studies you can find here: www.umu.se/en/usbe/education
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and/or dynamic approaches to detect them in the code or prevent their execution at runtime. Keywords for this project: code analysis, static analysis, reverse engineering, defense mechanisms
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will preferably relate to one or several of our five research profile areas in some way (please find information about the research profiles below). We are looking for a doctoral student who is creative
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. For questions regarding the position contact ryo.morimoto@umu.se or lena.svensson@umu.se. More about us You can find more information at www.umu.se/en/department-of-molecular-biology , www.mims.umu.se and