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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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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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model system to validate the findings from the bacterial assays and provide a proof of concept for NP-induced mutational and epigenetic effects in higher organisms. A translational objective is to compare
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partner or with another humanoid robot to solve a spatial problem (e.g. 3D puzzle, fold a paper). The tasks to be carried out are: (i) scene understanding: detection of objects and relations between objects
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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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. You can find more information about us at sixtlab , mims.umu.se , UCMR and department-of-molecular-biology We welcome your application!
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frequency of brain metastases. Early detection of metastases could dramatically improve survival rates. Liquid biopsies, which involve obtaining circulating tumour cells (CTCs), cancer cell free-mRNA and DNA
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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