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: Bachelor level background in physics, nanoscience or engineering Knowledge: Background in programming in Python (preferably also in any of the following ones: Labview, Matlab, C++, Igor Pro), preferably also
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project that covers topics such as materials science, encapsulation technologies, as well as in vitro validation and prototype validation in relevant environments. The candidate will work as part of
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and Devices (AEMD) group focuses on the material sciences and technology aspects of novel electronic materials, with a strong emphasis on graphene and other 2D materials such as MoS2. The group also
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fluid delivery and complete lab-on-a-chip integration for point-of-care devices. The application and technology transfer of the nanobiosensor devices for clinical diagnostics and environmental control is
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. Interface with other ICN2 departments (Strategy, Business and Innovation), researchers and external parties when required. Requirements: Degree in Science or Science Engineering (Chemistry, Physics or similar
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(solid and metastatic) and brain pathologies (Parkinson's, stroke, Alzheimer's, glioblastoma). The Nanomedicine Lab @ICN2 has strong links with the Faculty of Biology, Medicine and Health at The University
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experiments with the new setup. Requirements: Degree in Physics or Engineering Experience in Scanning Tunneling Microscopy instrumentation and operation is mandatory Candidates must be able to work
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As a flagship research center in nanoscience and nanotechnology, our mission is to open and explore new frontiers of knowledge at the nanoscale, and bring value to society in the form of new understanding, capabilities and innovation, while inspiring and providing broad training to the next...
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. Engage with educational institutions and the public to promote science awareness. Organize and contribute to science communication events that resonate with the educational sector and the broader community
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the following tasks: Assessment of sensor chip surface chemistry and biofunctionalization strategies. Development and optimization of the biosensor analytical performance, including the evaluation of assay