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the fundamental engineering understanding of gas centrifuge systems. The group leverages analytical techniques and advanced computational tools—including finite element analysis (FEA)—to evaluate and predict
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defects, are currently a major limiting factor for metal printing. In nanomedicine, various nanoparticles are used for controlled drug delivery and therapies, and laser-excited nanobubble-inducing shockwave
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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient
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encapsulation strategies to improve stability and controlled release of bioactive compounds. The project will also develop prototypes for human food, pet nutrition and cosmetic applications to validate
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sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with the microgrid model and
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to improving materials design and performance predictions for crash-relevant loading conditions, enhancing crashworthiness and structural integrity in lightweight automotive components. Your work will bridge
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transfer This research combines advanced numerical simulation and artificial intelligence to develop predictive models for high-temperature multiphase flows, with specific relevance to steel casting
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without shortages. The project will explore the use of historical demand and supply data, along with auxiliary information, within a Predict-then-Optimize (PtO) framework. This PtO framework will leverage
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. The objective of this PhD project is to develop AI methodologies for the analysis part of condition monitoring (CM) and predictive maintenance (PM). The primary challenge in predictive maintenance lies in
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is searching for a Control Engineer for developing health-aware model predictive control (MPC) for fuel cell hybrid electric vehicles (FCHEVs). Fuel Cell HEVs provide a long-term solution to