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Field
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analytical and numerical models of nonlinear vibration systems. The successful candidate will investigate stochastic resonance and noise-assisted dynamics, perform finite element modeling of coupled mechanical
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computing and artificial intelligence. Areas of interest include, but are not limited to, the following: Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks
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communicate predictions about human behaviour, contributing to trustworthy intelligent systems for high-impact applications. Job description Human behavior understanding is becoming an essential component
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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dehydroaromatization. The postdoctoral associate will play a central role in the processing, analysis, and interpretation of X-ray Absorption Spectroscopy (XAS) data collected on metal-modified zeolite catalysts under
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to metal organic framework materials. Therefore, the synthesis of MOFs and preparation of suitable spectroscopic samples is required of the incumbent. Characterization techniques such as fluorescence
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, genuine curiosity about leadership and organizations, and a collaborative spirit. Specifically, the ideal candidate has: A strong background in artificial intelligence, machine learning, computational
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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postdoctoral researcher in textile technology with a focus on pain management using textiles. The project explores so-called MH (machine-to-human) touch as a hypothesis for alleviating chronic pain. By
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science, cybersecurity, electrical and computer engineering, mathematics, physics, or a closely related field by the start date. Because this position is centered on quantum-era cybersecurity, we are particularly