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integration of heterogeneous technologies are increasing the complexity of power delivery, thermal management, and energy optimization across the entire computing stack. Emerging technologies such as fine
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strong background and interest in energy, and especially in thermal energy systems (for steam production), (optimal) control (MPC), physics-based modelling and optimization. Numerical optimization
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framework linking: PVD process → thin-film morphology → electrode microstructure → electrochemical behaviour → full-cell performance The two successful candidates will investigate how the morphology and
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reports and present findings to stakeholders and leadership. Support DFM & DFA review process. Continuous Improvement Identify opportunities for process optimization and cost reduction in packaging
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that integrate optimization algorithms, machine learning, explainable AI and interactive visual interfaces, enabling patients and healthcare professionals to understand, steer and collaborate with AI
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machine-learning optimization to reach broadband absorption are targeted. Secondments include 3 months at JCMWave for industry exposure and 6 months at TU/e (H. Caglayan) for nanofabrication and
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modeling, machine learning, reduced-order techniques, and computational fluid dynamics. The project will deliver the next generation of predictive tools for the design and optimization of low
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-principles modeling, machine learning, reduced-order techniques, and computational fluid dynamics. The project will deliver the next generation of predictive tools for the design and optimization of low