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Field
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funded NIH R01 project dissecting the SOX4/SOX9 transcriptional regulatory network that governs biliary development in Alagille syndrome and advancing the therapeutic potential of targeting this network in
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protective equipment performance assessment. This will involve using digital twin technologies, 3-dimensional body scanning systems, an array of field data collection sensors, robots, and other devices
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implement data collection protocols integrating biometric sensors (e.g., HRV, GSR, skin temperature), VR-based simulation environments, and mobile-based GEMA tools Coordinate and oversee participant data
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Memorial Sloan Kettering Cancer Center | New York City, New York | United States | about 5 hours ago
and tumor co-evolutionary networks in gastric tumors, tumor draining lymph nodes and metastases. We are seeking highly motivated candidates with advanced computational biology skills in the technical
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, shaker systems, laser sensors, force sensors, accelerometers, and a full suite of signal conditioning and data acquisition equipment to support advanced experimental studies in nonlinear vibration and
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place to understand heat exposure and stress include temperature/humidity sensors, wearable devices, and traditional survey administration. The group is poised to begin field implementation of heat
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sensor data in a materials science context. An excellent record of productive and creative research demonstrated by publications in peer-reviewed journal papers. Excellent written and oral
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pavement networks. This position will focus on modeling and optimizing pavement performance under evolving climate and hazard conditions. This position will advance modeling of pavement life-cycle
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(TMDH) project that aims to integrate NASA Earth observation (EO) products, in situ and field-sensor records, and advanced hydro-geologic models into an operational platform to support U.S.–Mexico treaty
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: Ph.D. in Physics, Computer Science or closely related areas Preferred: Demonstrated strength in one or more of the following. - Quantum many-body numerics: tensor-network methods, variational Monte Carlo