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, training deep learning models to adapt designs to boundary conditions, and integrating FEM workflows within parametric modeling environments like Grasshopper. The candidate will contribute to building a
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affecting the intestinal epithelium and the possible partners related to the different phases of the disease. The work will be a combination of ex vivo organoid cultures, in vivo disease models, combined with
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modeling of narrative, which includes developing new computational models of narrative and re-implementing historical models (also known as “storytelling systems”) so that they can be easily studied and used
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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, wearable and nearable sensor data, continuous glucose monitoring data, self-reported data, and multi-omics analysis to develop predictive models for steroid hormone disorders, particularly adrenal
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data
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leader will be the Head of Department. About the project Modern control systems rely on being at least partially predictive while digital twins also must maintain a state model of the targeted cyber
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the ability of the mono-ADP-ribosyltransferase, Parp7 and Parp11, at improving check point inhibition therapy in intraductal mammary tumour models. More about the position Research activities will take place in
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, and entrepreneurship. Doctoral Candidates will gain transferable skills and learn from industry role models, equipping them to make significant contributions to solving the AMR crisis. The succsesssful
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relevant component development tasks in the project Contribute to relevant simulation and modelling activities in the project Required selection criteria You must have a professionally relevant background in