37 wireless-sensor-networks-postdoc PhD positions at Delft University of Technology (TU Delft) in Netherlands
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future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which
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research spans from measuring and manipulating materials at the micro and nano scale, to the design of world-class sensors and actuators. You will be working in an international environment in one
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function itself remains intact. So, how do cells achieve this robustness when their underlying molecular networks diversify so dramatically over evolutionary time? Our lab has shown that in budding yeast
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: How does decoherence emerge in complex quantum systems? Can we emulate and study complex many-body physics? Can we use quantum coherence to realize novel improved sensors? Can we protect quantum states
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lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future
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candidates are particularly invited to apply, since the gender balance recently declined with the departure of quite some female postdocs and students. Your cover letter and CV may be internally shared with
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, and optical imaging is welcomed. We foster diversity and female candidates are particularly invited to apply, since the gender balance recently declined with the departure of quite some female postdocs
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the material consumption and environmental impact of energy generation. This PhD project is part of the MSCA Doctoral Network AWETRAIN (Airborne Wind Energy TRAining for Industrialization Network). Its objective
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foundational theory and real-world impact. Consortium This position is part of a European Doctoral Network consortium REUNATECH- NATECH Risk Management and Resilience of High-Tech Industries and Critical
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? No Offer Description Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15