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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 5 days ago
seconds in vivo, including acquisition, processing, and diagnostics. The post-doctoral fellow will work in close collaboration with an embedded systems engineer developing the software and electronics
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highly motivated and technically proficient Research Fellow in Control and Embedded Systems Engineering to support the development and deployment of intelligent, safety-enhanced BMS technologies within a
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that progresses and exploits the outcomes of your doctoral research - this is extension funding, not continuation of PhD research. Prior to applying, discussions regarding the planned research proposal must be held
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locations in the UK is essential. You will hold a PhD, have submitted your PhD or be about to submit your PhD in the next two months, or have equivalent experience, in in Public Health, Behavioural Science
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renewable and bio-based materials, and as part of the PEPR B-BEST initiative (Biomass, Biotechnology and Sustainable Technologies for Chemistry and Fuels), this PhD project is embedded in the WallMat
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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution
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the development and translation of rehabilitation technologies is strongly preferred. Prior experience with embedded systems or biomechanical evaluation in wearable devices is desired. Special Instructions Contact
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28 Aug 2025 Job Information Organisation/Company University of Bergen Research Field Geosciences Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application
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and thermal-aware design techniques for embedded systems. Contribute to research outputs and support timely completion of project milestones. Job Requirements: Preferably PhD in Computer Engineering
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. Job Requirements: Preferably PhD in Computer Science or related field. Background and familiarity with the implementation and deployment of machine learning pipelines in embedded systems (e.g., robotic