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5 Sep 2026 Job Information Organisation/Company Tilburg University Research Field Computer science » Computer hardware Computer science » Programming Engineering » Electrical engineering Engineering
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scientific initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and
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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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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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well as biologists. This in turn requires complete honesty and ease in revealing which fields the candidate is not an expert in, such that other team members can teach and support them Desirable: Having taken courses
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). Participate in grant writing for the addition, upgrade and replacement of CCIC mass spectrometry hardware. Communicate with and promote MS capabilities to Ohio State and national CCIC MS users by email, up
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SD-26109- POSTDOCTORAL RESEARCHER IN AI-BASED ENERGY MANAGEMENT OF RESILIENT MICROGRIDS WITH SECO...
systems and microgrids - Battery energy management systems - Renewable energy integration - Control systems - Artificial intelligence or machine learning for energy applications Experience and skills
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designing and building hardware solutions, e.g. using Arduino, Raspberry Pi, 3D-printing, CAD and CNC machining and/or laser cutting Language Requirements: Excellent command of English (C1), both orally and
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, Microelectronics, Computer Engineering, or a closely related field, completed by the start of the position Have a solid background in digital hardware design: Verilog/SystemVerilog RTL, logic synthesis, and place
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applicability. By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse