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. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? The AI Readiness and Assessment (AIRA) research group at LIST is seeking a highly motivated
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compared to "classical" on-body sensors - design techniques for intra-oral medical sensors are much more stringent than for on-body sensors. For reference please read the following article https://dl.acm.org
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | about 2 months ago
and refining their dynamic models through parameter identification on dedicated test benches, including external measurement systems (encoders, force sensors). The refined models will serve in
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-constrained devices such as wearables, smart sensors, hearables, and IoT nodes. While current deployment methodologies can optimize models before deployment, the resulting software remains static throughout
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of gravitational wave instrumentation. You will join the group's research on sensors & actuators, seismic attenuation and controls. Seismic noise is a dominant low-frequency limitation for ground-based gravitational
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passive seismic experiments (HOREX® and Full-HOREX®). These deployments, involving more than 2,000 nodal sensors, provide high-resolution imaging of the subsurface over an 80 × 80 km area to a depth of ~20
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focusing on elucidating neural (e.g., EEG/ERPs, MRI), behavioral, and real-time markers (e.g., passive sensor data, EMA) of depression and suicidal behaviors in adolescents. The positions are full-time
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technology for decarbonized mobility. However, their safe and sustainable deployment in dense urban traffic remains challenging due to uncertain vehicle–battery dynamics, sensor or actuator faults, critical
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on sensors & actuators, seismic attenuation and controls. Seismic noise is a dominant low-frequency limitation for ground-based gravitational wave detectors, and is mitigated by multi-stage attenuation systems
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cutting-edge computer vision, wearable sensors, and citizen science, RUN2GETHER will capture large-scale, real-world data during running events and group runs. These data will advance our understanding