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competing priorities without supervision High level written and spoken English A willingness to contribute to the work of others by offering practical and intellectual help Be enthusiastic, committed, hard
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partners as well as ports through Living Lab Networks. Your contributions will include: Doing cutting-edge research in IoT and IoUT devices, communication, and system Publishing your research in high-impact
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well as contributing to other research and advisory activities in our research group. You’ll work hands-on with high-end GC-MS instrumentation (and other analytical equipment) and collaborate closely with researchers
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framework. The extensions will allow the evaluation of high-impact interventions including dynamic road pricing and Mobility-as-a-Service (MaaS) solutions. FEDORA is also advancing the state-of-the-art in
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in high-level conferences and publish in high-quality journals as part of your dissemination tasks. As a postdoc, you will mainly focus on: Developing advanced control strategies for buildings
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for experimental research within molecular biology, strain engineering, high throughput screening and fermentation at different scales. Responsibilities and qualifications Focus is on genetically engineered bacteria
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computational nanophotonics High level of understanding of the diffractive properties of nanostructured dielectric optical surfaces Experience with inverse design for nanophotonics. Experience with optical
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experimentally validating their research. Publishing and disseminating their research in high-quality conferences and journals. Contributing to deliverables and other administrative activities associated with
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to mucosal dysfunction. Mucins remain underexplored despite their significant role in numerous health conditions, making this a high-risk, high-reward research opportunity. You will explore novel therapeutic
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, protocols, and data standards across collaborating institutions and scales. This collaboration will support the generation of coherent, high-quality datasets and enable the development of predictive models