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/augmented/extended (VR/AR/XR) environments to support learning of scientific concepts and practices at the university-level. The main aim of this work package is to investigate how such cutting-edge
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for using AI to develop social engineering attempts. This project combines human subject research of learning and decision making, Human-Computer Interaction, and the advancement in AI methods
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of inverse photonic design, machine-learning (adjoint or topology optimization), or other advancing design methodologies. Ability to design and model active photonic devices (MEMS, electro-optic or thermo
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to the development of sustainable materials for the hydrogen economy. You are an independent thinker, eager to learn new experimental techniques, and enjoy collaborating with researchers from different disciplines as
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be used to combine these datasets while accounting for their different spatial scales, uncertainties and sampling frequencies. Machine-learning methods may also be explored for retrieval, bias
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improved using machine learning techniques. The developed techniques will be applied to metrology of semiconductor samples. Job requirements You are an enthusiastic candidates with a ‘drive’ for applied
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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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Classification Title: Research Assistant Scientist Classification Minimum Requirements: Ph.D. in Electrical Engineering, Computer Science, Computer Engineering, or a closely related field by
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and