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Field
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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Research scientists, graduate students, and undergraduate students. Applicants should have experience in developing and characterizing sensors, structural health monitoring systems, and self-healing
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, with publications in high-impact journals. Excellent writing and communication skills in English аrе key assets. Expertise in bio- and/or photoelectrochemical sensor development is a plus. What we offer
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algorithms for the scanner using machine learning and deep learning. Qualifications: The position requires some background in machine learning, optimization, and deep learning. Some familiarity with MR physics
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of the following: quantum field theory, quantum information, out-of-equilibrium dynamics, theoretical cosmology, particle physics, gravity, or quantum algorithms. Candidates are expected to demonstrate
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, especially the toroidal spectrometer, construction and commissioning of the MOLLER detector at Jefferson Lab ● Development of the SOLID software framework, tracking algorithms, DAQ subsystem and various
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will have the opportunity to develop innovative algorithms and models that integrate multiple data modalities, collaborate with industry partners, and contribute to high-impact publications. Job
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or upcoming year (optional) Experience in one or more of the following areas: experimental data analysis related to hadronic physics, polarized targets or beams, silicon sensors, calorimetry, detector
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their different sensors for decision making. Humans will be able to seamlessly instruct these robots or directly collaborate with one or a team of robots. The fellow will have the opportunity to advise individuals