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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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created from extracellular matrix (ECM)-based hydrogels or from porous titanium will be fabricated using additive manufacturing and designed to incorporate antimicrobial agents to reduce the risk of
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safety. The goal of this PhD project is to increase knowledge of thermodynamic and material processes in marine hydrogen propulsion systems and to develop predictive models that ensure safe and efficient
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fueling of hydrogen will be tested. These data will be used to validate the developed models and guide infrastructure design. You will collaborate with other PhD students and researchers from Uppsala
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have some experience with scientific programming, in particular in Python, PyTorch or similar, in particular with respect to advanced data analysis or modelling operations. PhD students at the department
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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develops an adaptive AI-guided XR platform for capturing and transferring expert manufacturing knowledge. Your focus will be on developing AI methods for analyzing and modeling human workflows based on data
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-based techniques, based on nano-DESI and electrospray ionization, will be developed further and coupled to modern mass spectrometers. As a PhD candidate you will be involved in all the steps
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-or script-based), and metadata integration. Demonstrated expertise in both untargeted and targeted metabolomics workflows and strong understanding of statistics and statistical modeling applied