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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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welcome you to apply for a DDLS PhD position in Data-driven cell and molecular biology at the Department of Information Technology, Uppsala University. The Department of Information Technology holds a
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cultures—a powerful 3D ex vivo model—this project will dissect the mechanistic links between mTOR signalling, reactive glial phenotypes, and complement activation. The project will also incorporate human
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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The bacterium Clostridioides difficile is an important cause of diarrhoeal illness, particularly in the elderly and those undergoing antibiotic therapy. C. difficile infections (CDI) are difficult to treat and prevent due to the ability of C. difficile to produce robust spores that can survive...
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failure before components are built? We invite applications for a fully funded PhD project to develop microstructure-aware simulation models for fatigue and damage prediction in turbine wheels. Working in
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numerical modelling framework to simulate how light and heat interact with the target body tissue, while also incorporating neural signalling dynamics to explore how light-based stimulation affects
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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real-world applications? Are you fascinated by how the local dynamics of nearest neighbour models can determine the macroscale behaviour of a system? Do you want to understand how matter self-organizes
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project