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extensive magnetic and electrical characterization of 300mm fabricated devices, potentially with support of micromagnetic simulation. In this course, alternative materials and methods will be developed using
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simulate maternal cardiovascular response Data fusion and integration of heterogeneous datasets from wearables and hospitals A Collaborative, Interdisciplinary Environment: The position is based in
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Research Groups at the Faculty of Engineering, which conduct cutting-edge research into electric propulsion systems, composite materials, and advanced simulation technologies. Vision We are seeking a highly
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 4 days ago
candidate, in case of doubt, to provide supporting documents for their statements. False statements made by candidates will be sanctioned in accordance with the law. The jury will draw up minutes of its
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of Technology Additional info: Candidates interested in participating are kindly requested to send the following documents via email (to: yauhen.aniskevich@polsl.pl ): 1. Copy/scan of Master’s degree diploma, and
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/nanofabrication, l’électronique de puissance, l’encapsulation et l’intégration électroniques. • Expérience avec les outils de simulation numérique et les outils de calculs : Ansys HFSS, Matlab etc. • Fort intérêt
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simulation study of light matter interaction, digital twin enabled process development and life cycle assessment will be researched. Opens: Immediately Deadline: 08/08/2025. Duration: 36 months Funding: Funded
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into electric propulsion systems, composite materials, and advanced simulation technologies. Vision We are seeking a highly motivated PhD student to join our interdisciplinary team to help address critical
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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discipline boundaries and abstraction levels is a must. Knowledge in integrated circuit design, testing and simulation using Cadence is a plus. Knowledge of digital neuromorphic hardware and sensors is a plus