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materials, thin film technologies for neural interfaces, as well as in-vivo device validation of the neural technologies. Requirements: MSc, PhD in Materials Science, Nanotechnology, Engineering, Physics
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. This contract has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101007417 - NEP. Equal opportunities: ICN2 is an equal opportunity employer
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novel energy harvesting devices for the development of self-driven neural interfaces. Requirements: PhD in Energy, Electrochemistry, Materials Science, Nanotechnology, or equivalent degrees. Knowledge and
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funding from the European Union's Horizon Europe research and innovation programme under grant agreement No101115204- ICONIC through the European Innovation Council (EIC), call HORIZON-EIC-2022
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activities: languages, mentoring programme, wellbeing programme. International environment Estimated Incorporation date: June-July 2025 How to apply: All applications must be made via the ICN2 website and
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, among others.) Training activities: languages, mentoring programme, wellbeing programme. International environment Estimated Incorporation date: as soon as possible How to apply: All applications must be
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contracting some products (health insurance, childcare, training, among others.) Training activities: languages, mentoring programme, wellbeing programme. International environment Estimated Incorporation date
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establish an independent research program focused on optical and optoelectronic measurements of advanced quantum materials and devices, with potential applications in photodetection, sensing, communication
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, mentoring programme, wellbeing programme. International environment Estimated Incorporation date: as soon as possible How to apply: All applications must be made via the ICN2 website and include the following
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and cleanroom infrastructure. The candidate must possess the technical expertise to ensure the smooth daily operation of equipment, troubleshoot issues, and implement an effective maintenance program to