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property distributions from process-induced microstructure variations using the ML models created. Work with the extended team to link the simulation of sensor data with new multi-scale processes
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the development and testing of advanced control strategies for building energy systems. The work is closely connected to a real-world pilot case, where measured sensor data will be used for model calibration
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about the lab and their work can be found by visiting https://sarvestanilab.com/ About the Postdoctoral Scientist role: We are seeking a motivated and creative Postdoctoral Scientist to join our dynamic
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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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details. Familiarity with electrical and power systems, energy storage systems, EV charging systems, test & measurement instruments, sensors/sensor networks, and PLC systems is preferred. Prior experience
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technical theatre, live event production, or venue management. Preferred Qualifications: Fluency within the ETC Eos ecosystem, lighting networks, Paradigm, and ETC Sensor 3. Competency in programming
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skills in designing advanced neural networks (including Vision Transformers, Graph Convolutional Networks, and Spiking Neural Networks), hardware implementation of algorithms for reconfigurable FPGA
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), centered around the development of cutting-edge optomechanical sensors. The position is embedded within QSTeM, a recently established Testbed for mechanical sensing, where novel sensors are designed
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-binding domain and leucine-rich repeat (NLR) genes play important roles as the sensors/receptors of non-self molecules and in activation of immune responses such as transcriptional reprogramming and cell
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vulnerable and mobility impaired individuals, and case study analyses. Proposals that use digital technologies in data capture are welcomed, for example GPS (location data) and other sensors that measure