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of passive radiative cooling in daylight (net cooling power and temperature differences). Design and execute experiments to compare thermal performance under controlled and real conditions. Develop and operate
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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characterization to device integration. You will fabricate and test cutting-edge device architectures, including 2D Complementary FETs (CFETs), memory devices, and sensors, pushing the boundaries of what's possible
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Role: Postdoctoral Research Associate in 2D SPD Arrays for Quantum Networking Grade and Salary: Grade 7 - £37,694 - £47,389 FTE and working pattern: Full time (35 hours per week), Fixed term (18
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, international and interdisciplinary environment . PhD-students have the opportunity to take advantage of our network of excellence , consisting in partners of national and international research institutes and
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University of California Agriculture and Natural Resources | Davis, California | United States | 24 days ago
farming practices. The Specialist will examine the potential of agricultural technologies, such as drones, spectral analysis, and sensors, to help California growers ensure the safety of their harvest while
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with sparse aerial and dense terrestrial sensors for enhanced 3D spectrum use prediction. DC3: RF-sensing for ambient IoT devices and coexistence with legacy devices Host: IMDEA Networks Institute
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PHD DEVELOPMENT AND EXPERIMENTAL VALIDATION OF UNDERGROUND SAND-BASED THERMAL ENERGY STORAGE SYSTEMS
mechanical stability. You will support data acquisition and sensor setup for temperature profiling, model calibration and validation. You will apply multi-physical numerical modeling to develop 3D coupled
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range (beyond 24 keV): measure and validate reference data in synchrotron beamtimes together with our international collaborators. Advance solid-state monitors: deploy new diamond sensors, model
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, ensemble Kalman filters, and physics-informed neural networks (PINNs) enforce conservation laws while fitting observations. The key is to apply the vast amount of physical insights developed in turbulence