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Field
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. As part of a multidisciplinary team spanning multiple Argonne divisions, you will contribute to the design, fabrication, and characterization of superconducting devices based on high kinetic inductance
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instrumentation. The goal of the project is to design and develop a wearable ultrasonic imager, combined with optical measurements, for monitoring uterus contractions during pregnancy to detect the early signs
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Preferred Qualifications: We are interested in candidates with general research experiences in quantum optics and quantum information science. Priority is given to candidates with experience on the design
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ultrasound–optical imaging, contrast agent design, and computational reconstruction, with applications in deep-tissue imaging, tumor microenvironment characterization, and functional sensing. This position
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
and Environmental Engineering at the University of New Hampshire. The successful candidate will be responsible for developing and coding workflow processes and designing experiments for multisensory data
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of short stable recycling cavities, which requires significant modifications of the existing vacuum and seismic isolation systems. The optics and benches will be suspended through new compact seismic
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spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions
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Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) | Leipzig, Sachsen | Germany | 3 months ago
MVPA, RSA, pattern classification, or related techniques Interest in or experience with computational modelling Strong programming skills Excellent academic track record Ability to work both
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the electronic, magnetic, and optical properties of 2D materials at ultrafast timescales, which holds promises for developing new energy technologies. The candidate is responsible for conceiving, planning, and
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Activities (PEAs) and their outputs. In response, this project will develop strategies for making PEAs more visible in OS monitoring exercises, with an eye toward supporting greater recognition and reward