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. Experience in at least two of the following areas: Robotics and automation Mechatronics and embedded systems Experience with sensor integration, calibration, experimental measurement systems, and automated
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PhD education, for example through relevant work experience and/or peer-reviewed academic works. Experience in at least two of the following areas: Robotics and automation Mechatronics and embedded
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on the field can be derived from first principles, and how these constraints can improve the technique's performance, particularly when embedded in modern machine learning models. The ultimate goal is to
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embedded in a national project with close collaboration with industry. Responsibilities and tasks The purpose of the project is the design and development of novel photonic WDM switches and the fast control
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at the intersection of wireless communications, networking, embedded systems, and satellite technologies. Your research will contribute to the development of reliable, resilient, and sustainable
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mechanisms, and secure AI acceleration for next-generation embedded and edge platforms. Research objectives: Develop FPGA-based emulation and prototyping frameworks to accelerate the design-space exploration
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implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable solutions. Project description E2PACKMAN is a
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environment within the Plasma & Materials Processing group, embedded in TU/e’s strong Brainport ecosystem, as well as tightly connected to other academic partners and industry through national research
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distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable solutions. Project description E2PACKMAN is a €93.7 million European
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools