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27 Aug 2025 Job Information Organisation/Company DIFFER Research Field Engineering » Electrical engineering Engineering » Electronic engineering Physics » Applied physics Physics » Electromagnetism
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The Dutch Institute for Fundamental Energy Research (DIFFER) performs leading fundamental research on materials, processes, and systems for a global sustainable energy infrastructure. We work in close partnership with (inter)national academia and industry. Our user facilities are open to...
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reproducible sample preparation method for cryogenic electron microscopy (cryo-EM). You will fabricate microfluidic mechanical probes with phase guides, develop controlled dispensing of femto-liter volume
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Delft, and CryoSilico cryo-EM sample supports from DENS solutions to develop a novel reproducible sample preparation method for cryogenic electron microscopy (cryo-EM). You will fabricate microfluidic
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(m/f/d) in the topic: “AI-based processing of CAD models for automated planning of computer-aided manufacturing.” The candidate has the opportunity to pursue a doctoral degree (Ph.D.). Remuneration is
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to the research project. Please send the email expressing interest to Coralie.English@newcastle.edu.au by 5pm on 30 October 2025. Applications Close 30 October 2025Apply Now PhD and Research MastersFind out more
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) Electronic application form for the competition procedure; b) Copy of certificate or diploma of the PhD degree, or recognition/registration of the PhD degree; c) Detailed curriculum vitae structured to allow
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3 Fully Funded PhD Students positions!📍 Remote Sensing Laboratory, McMaster University, Hamilton, Ontario, Canada🗓 Start Date: Spring or Fall 2026The Remote Sensing Laboratory at McMaster
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the our webpages here: PhD in artistic research (khio.no) For questions related to submission of application material to the application portal, please contact HR, email: rekruttering@khio.no. Please mark
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training