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, the PhD researcher will develop physiological-model-based artificial intelligence technologies to assess patients’ recovery process, detect or even predict the occurrence of clinical adverse events like
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pathways to turning points in infrastructure use through a systemic modelling approach. Essential activities within your project will be to: identify policy mechanisms that can change infrastructure
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on conceptualizing, fabricating, and characterizing effective memristive devices, with the goal of forming device networks for realizing the physical learning paradigm developed at AMOLF. You will integrate
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on a coherent set of conceptual, empirical, and applied studies. Additional Information Benefits A full-time, 4-year PhD position is available. We offer excellent mentorship in a stimulating research
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design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during
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claims. The project will develop and test a Washing Detection Model to better understand when and why consumers are vulnerable to washing. In the second phase of the project, you will design and evaluate a
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new models and methods. In this PhD project, you will: develop dynamic supply chain design models that enable the repositioning of AM equipment based on Defence requirements in conflict zones and other
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vivo evaluations using soft-tissue models to simulate gastrointestinal navigation and refine the system’s interface and functionality based on clinician feedback. Contribute to preclinical deployment
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environments. The project will build on an interdisciplinary approach, combining concepts and methods from physics, chemistry, and mathematical modelling. The long-term goal is to derive design principles
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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc