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Field
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actionable insights (e.g., visualizations, algorithms) in real-time. The MLE will join a dynamic, multi-site team working at the intersection of machine learning, digital phenotyping, pediatric mental health
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in the live organism to study e.g. immune responses or cancer development. Moreover, using molecular sensors we also aim to read out cellular functionality in vivo e.g. metabolic reprogramming in
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, administrators, or patients • Integrating multi-source, high-dimensional datasets (EHR, claims, environmental/socioeconomic data, sensor data) • Contributing to translational research that tightens the loop
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machine learning. This encompasses application domains such as industrial inspection, (ultra) high-definition video enhancement, smart multi-camera networks, computer vision, sensor fusion, and (medical
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Community (IC) mission areas. In addition to developing sensor systems across complex RF environments, you will develop prototype communication systems—implementing end-to-end transmitter/receiver chains
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and collaborative research focused on incorporating sensing technology in the early childhood education space. “Sensing technology” refers to tools that use sensors to acquire information about human
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based methods to observe low affinity interactions directly and relate them to structural and functional outcomes. In parallel, you will develop new sensors for intracellular potassium concentration
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networks, Internet of Things sensors, and analytics platforms that gather data from those infrastructures, as well as telecommunications networks. To fully support the operation of cities, telecommunications
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intelligence (AI) algorithms applied in virtual reality (VR) environments using data gathered from wearable and non-invasive sensors of different types suitably and purposely put together. The expected output is
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and to better understand the physiological mechanisms of resistance to abiotic constraints. The acoustic signature, integrated into the algorithm controlling the autonomous acoustic sensors, will