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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices
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system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing, and machine-learning-based analytics. The research work at NTNU will focus particularly
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, and machine-learning-based analytics. The research work at NTNU will focus particularly on automation, robotics, mechatronic design, sensor integration, and intelligent experimental systems required
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modern machine learning, statistical signal processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs
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at Newcastle University invites applications for a Research Assistant/Associate position in Hardware–Software Co-design for Heterogeneous Integrated Circuits within the Microsystems Research Group
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hardware prototypes. Collaborate with interdisciplinary research teams spanning artificial intelligence, circuit design, computer architecture, and embedded systems engineering.
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robot learning and edge deployment. Closing date: 15 August Overview Robotics is entering a new phase where foundation models connect perception, language and action. Vision-language-action models, robot
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technologies Research Field Computer science » Computer systems Computer science » Computer hardware Researcher Profile First Stage Researcher (R1) Application Deadline 11 Aug 2026 - 21:59 (UTC) Country Denmark
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optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware