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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation group. Main responsibilities Conduct research in collaboration with senior researchers and
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and synchrotron X-ray microtomography; experience with 4D (in-situ/operando) X-ray microtomography experiments; experience in developing 3D/4D image data analysis algorithms; experience using 3D/4D
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both academic research and industrial applications. In addition to theoretical research, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation
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– protein interactions or enzyme optimization. Main responsibilities The successful candidate will use and develop methods within one, or preferably multiple, of the following categories: Sequence library
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repertoire sequencing to examine vaccine-induced antibody and B cell responses in rituximab-treated multiple sclerosis patients across three doses of the BNT162b2 mRNA vaccine. The goal is to identify key
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mathematics, ecology, history, climatic and medical sciences in collaboration across multiple institutes. An integral part of the project is to develop process-based eco-epidemiological models considering
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and in vivo model systems, applying multiple omics methods. You will be working with clinical samples, method development and several molecular biology techniques, especially PCR and sequencing as